Daily Report — 2026-02-17

Daily Overview

• What was done: Five parallel work streams: DCC resolved fundamental MIHD coordinate bug improving visual ARI from 0.065 to 0.12-0.25; MacBook/TzJsDesktop iteratively developed and used daily report tool processing historical logs from 2026-02-12 through 2026-02-16 across four devices; tianhe advanced Error Recovery Benchmark from 251 to 454 scenes with BC-RNN training, VLA dual-server deployment, and multi-policy evaluation framework; TzJsDesktop diagnosed and fixed ccusage pricing bug revealing 13x cost underestimation; tianhe established SSH stability infrastructure for remote development
• How it was done: DCC corrected CSV column mapping, cleared caches, reran 286 experiments; MacBook/TzJsDesktop implemented two-phase export→merge architecture with rclone sync, robust JSON parsing, conversation summaries, and Hugo deployment; tianhe fixed MuJoCo API compatibility, trained BC-RNN 600 epochs, implemented obs key mapping adapter, deployed VLA servers on Pi0/Pi0.5, debugged integration with 79 unit tests + GPU validation; TzJsDesktop implemented ccusage fallback pricing mechanism and re-exported 5 days of logs; tianhe configured SSH stability (tmux+keep-alive+auto-reconnect) and documented local client setup
• Impact: MIHD data quality fundamentally restored (all vision-based results now valid); daily report tool achieved production maturity with multi-device workflow enabling systematic knowledge retention; Error Recovery Benchmark surpassed M5 scene generation target (227%) and completed first trained policy with multi-policy evaluation infrastructure ready (M6 milestone), though BC-RNN showed 0% SR revealing undertrained checkpoint issue; SSH stability eliminated remote session loss; ccusage fix corrected displayed costs from $0-7/day to actual $18-48/day, restoring financial tracking integrity

DCC

• What was done: Discovered and fixed MIHD load_spatial_coordinates() X/Y coordinate swap bug where pixel_x was actually pxl_row (Y-axis) and pixel_y was pxl_col (X-axis), causing all vision patches to be extracted from transposed image positions with visual ARI only 0.065
• How it was done: Modified CSV column name mapping from [’…’, ‘pixel_x’, ‘pixel_y’] to [’…’, ‘pxl_row’, ‘pxl_col’] and coordinate tuple from (pixel_x, pixel_y)=(Y,X) to (pxl_col, pxl_row)=(X,Y); deleted all spatial_coords.npz and vision/ cache; re-extracted embeddings for 11 sections across UNI2/HIPT/ResNet50; validated coordinate ranges (X: 323-1637, Y: 387-1773) and 100% embedding uniqueness
• Impact: UNI2 visual ARI improved from 0.065 to 0.08-0.25 (sections 151673-151676 reached 0.22-0.25); all fusion experiments dependent on vision will be corrected; this was a root data integrity bug affecting entire benchmark validity

MacBook

• What was done: Primary hub for daily report tool usage: processed conversation logs from 2026-02-12 through 2026-02-16 across four devices (DCC/tianhe/TzJsDesktop/MacBook) covering MIHD, Error Recovery Benchmark, CalendarPro, and gadget projects; published reports to Hugo bugJournal section and GitHub Pages
• How it was done: Executed export –summarize to pull local logs → merge –sync to download other devices’ exports via rclone → called Claude CLI API to generate structured JSON/Markdown reports → wrote to website/content/bugJournal/ → ran update.sh to build site (139→141 pages) → pushed public/ to GitHub Pages
• Impact: Established complete multi-device documentation pipeline from raw conversation logs to public website; created systematic historical record for 5+ dates enabling long-term knowledge tracking; GitHub Pages expanded from 136 to 141 pages

TzJsDesktop

• What was done: Configured SSH stability infrastructure for tianhe HPC cluster access with three-layer defense; served as workstation for daily report tool usage analyzing historical logs spanning error-recovery-benchmark, MIHD, CalendarPro, and gadget projects; diagnosed and fixed ccusage pricing bug revealing 13x cost underestimation
• How it was done: Updated ~/.ssh/config with connection multiplexing (ControlMaster/ControlPath/ControlPersist), optimized keep-alive (ServerAliveInterval 15s, ServerAliveCountMax 6), created ~/bin/auto-ssh wrapper script with retry logic and tmux session attachment; repeatedly invoked summarize tool to generate 10+ reports; investigated ccusage source code and LiteLLM pricing database, implemented _FALLBACK_PRICING dictionary and _fix_zero_cost_models() function, corrected modelName field reference, re-exported Feb 13-17 logs
• Impact: Eliminated SSH disconnection issues preventing long-running HPC tasks; drove daily report tool feature enhancements through extensive dogfooding; corrected claude-opus-4-6 pricing from $0-7/day to actual $18-48/day, revealing ~$150+ cumulative error and restoring financial tracking integrity

tianhe

• What was done: Advanced Error Recovery Benchmark M5/M6 milestones: expanded scene database from 251 to 454 through friction and pose_perturb generation, trained BC-RNN policy (600 epochs on A800), fixed obs key mapping integration bug, set up VLA dual-server infrastructure (Pi0 port 5556, Pi0.5 port 5557), debugged multi-policy evaluation framework with 79 unit tests + GPU validation, established SSH stability solution, updated project documentation to v4.6
• How it was done: Fixed three non-impulse injectors’ MuJoCo API compatibility (mj_name2id→body_name2id with _main suffix handling); generated 103 pose_perturb + 100 friction scenes via _augment_specs_with_alternatives(); trained BC-RNN via robomimic in ~15 minutes; implemented object-state → object key mapping in policy_adapter.py with Strategy 1 and Strategy 3 fallback; validated with 79 unit tests and GPU integration test (5 rollouts, KeyError eliminated); generated visualization videos revealing 0% SR due to undertrained checkpoint; deployed VLA servers ready for evaluation; configured tmux + SSH keep-alive + proxy isolation; created claude-tmux script and local client setup guide
• Impact: Surpassed M5 target (200→454 scenes, 227% of goal); non-impulse injectors transitioned from theoretical to functional; BC-RNN became first trained baseline policy with integration bugs resolved; VLA dual-server infrastructure ready for multi-policy evaluation (M6 milestone); SSH stability issues permanently resolved enabling reliable remote development; visualization revealed model quality issue (not framework bug) requiring better checkpoint or more training

Fixed critical MIHD vision encoder X/Y coordinate swap bug requiring 286 experiment reruns, extensively developed and used multi-device daily report tool to process historical conversation logs from five dates across four projects, advanced Error Recovery Benchmark M5/M6 milestones achieving 454-scene database with BC-RNN training and VLA dual-server integration, resolved SSH stability issues for remote development, and corrected ccusage pricing bug revealing 13x cost underestimation (~$150+ cumulative error).

Tasks

Architecture & Strategy

• ✅ Fix MIHD load_spatial_coordinates() X/Y coordinate swap bug — Corrected CSV column mapping where pixel_x actually referred to pxl_row (Y-axis) and pixel_y to pxl_col (X-axis), causing vision patches to be extracted from transposed positions. Modified scripts/run_benchmark.py and utils/data_loader.py to use correct (pxl_col, pxl_row)=(X,Y) order.
• ✅ Generate tianhe M5 non-impulse error scenes (pose_perturb + friction) — Created benchmark_v4_nonimpulse.yaml config; generated 103 pose_perturb + 100 friction scenes via _augment_specs_with_alternatives() expanding database from 251 to 454 (achieving 227% of M5 200-scene target); gripper_bias generation pending but not blocking
• ✅ Diagnose and fix ccusage claude-opus-4-6 zero-cost billing bug — Investigated ccusage source code, LiteLLM pricing database, and actual log data. Confirmed claude-opus-4-6 model name cannot match LiteLLM’s anthropic.claude-opus-4-6-v1 entry, causing cost field to always be 0. Implemented fallback pricing mechanism in daily_summary.py: added _FALLBACK_PRICING dictionary (opus-4-6: input $5/M, output $25/M, cache_creation $6.25/M, cache_read $0.50/M) and _fix_zero_cost_models() function for post-processing. Fixed field name bug (mb.get(‘model’) → mb.get(‘modelName’) or mb.get(‘model’)). Re-exported logs for Feb 13-17 (5 days), correcting displayed costs from $0-7/day to actual $18-48/day, revealing 13x underestimation (~$150+ cumulative error).
• ✅ Rerun MIHD vision single-modal experiments to validate fix — Re-extracted 11 sections’ vision embeddings (UNI2/HIPT/ResNet50, 33 tasks total); verified coordinates correct (X: 323-1637, Y: 387-1773), embeddings 100% unique, UNI2 ARI improved from 0.065 to ~0.12 (sections 151673-151676 reached 0.22-0.25)
• ✅ Implement daily report tool core two-phase architecture — Created summarize/daily_summary.py supporting three data sources (Claude Code JSONL, ChatGPT JSON, generic), two-phase export (local device) + merge (cross-device aggregation) workflow, LLM-based structured report generation, and multi-API support (claude_cli/anthropic/openai)
• ✅ Fix tianhe non-impulse injector MuJoCo API compatibility — Changed friction/pose_perturb/gripper_bias injectors from mj_name2id() to model.body_name2id()/.geom_name2id(), added _resolve_body_id() helper handling robosuite’s _main suffix convention. Wrapped in try/except for graceful handling of missing bodies.
• ✅ Train and integrate tianhe BC-RNN baseline policy — Searched for MimicGen pretrained checkpoints (none exist). Trained BC-RNN from scratch: 2-layer LSTM, GMM action head, 600 epochs on MimicGen pick_place dataset, 14.3MB checkpoint saved to bc_rnn_checkpoints/. Fixed obs key mapping bug by implementing object-state → object aliasing in policy_adapter.py for Strategy 1 (raw obs) and Strategy 3 (StateExtractor fallback). Validated with 79 unit tests and GPU integration test (5 rollouts, KeyError eliminated). Generated visualization videos revealing 0% SR due to undertrained checkpoint (robot hovers but doesn’t grasp), confirming model quality issue not framework bug. Updated benchmark_v4.yaml model path.
• 🔄 Rerun MIHD vision-dependent fusion experiments (core_multimodal_fast) — Launched core_multimodal_fast experiment group to re-extract STAIG fusion UNI vision embeddings (staig_strict mode); UNI extraction completed for 11 sections, evaluation phase running
• ✅ Add multi-device rclone synchronization to daily report tool — Implemented _rclone_upload with subdirectory support (logs/ for exports, reports/ for merged results), _rclone_download_logs for cross-device fetch, merge –sync flag for automatic aggregation, config –show for remote inspection
• ✅ Add conversation_summaries section to report schema — Extended JSON schema to include per-session summaries with project name, source, timestamp, topic (≤60 chars), 2-4 sentence narrative, outcome status (completed/partial/exploratory/abandoned), level, and importance. Updated SUMMARY_PROMPT and markdown generator
• ✅ Configure SSH stability for tianhe cluster access (client and server) — Server-side: configured tmux session persistence, SSH keep-alive (15s interval/90s tolerance), claude-tmux one-command script, session-scoped proxy control. Client-side: updated ~/.ssh/config with connection multiplexing (ControlMaster/ControlPath/ControlPersist 600s), optimized keep-alive (ServerAliveInterval 15s, ServerAliveCountMax 6), created ~/bin/auto-ssh wrapper script with automatic tmux session attachment and retry logic (5s interval, 100 max retries). Enhanced .tmux.conf with history and stability settings. Created docs/local_ssh_setup_guide.md with VS Code settings.
• ✅ Set up tianhe VLA dual-server evaluation infrastructure — Extended collector.py with VLA support: added _policies_needing_images tracking, modified _get_obs() for camera images, implemented predict_from_obs() interface, updated load_policy() for vla_server type. Extended 3_collect_data.py with VLA policy arguments (–vla_pi0_port, –vla_pi05_port). Fixed Pi0.5 observation image key mapping and norm_stats path (Franka → LIBERO workaround). Deployed VLA servers: Pi0 on port 5556 GPU 1, Pi0.5 on port 5557 GPU 3. Random policy working normally, BC-RNN integration complete, VLA servers ready for evaluation.
• 🔄 Multi-policy evaluation pipeline execution (M6 milestone) — Running evaluation with 4 policies (Random + BC-RNN + VLA_Pi0 + VLA_Pi05) on 50 scenes × 3 seeds. Created scripts/5_baseline_accuracy.py for zero-error baseline testing (Random + BC-RNN, 20 rollouts). All infrastructure ready, evaluation in progress.
• ✅ Update Error Recovery Benchmark documentation to v4.6 — Updated 项目全景总结.md with 12 edits: version numbers, milestone table (M5/M6/M9), scene statistics (271→454), documented pose_perturb/friction injector implementation via _augment_specs_with_alternatives(), VLA dual-server architecture (Pi0 5556 + Pi0.5 5557), M5 milestone achieved, M6 status, new §13 on zhaoganlong Pi0.5 training infrastructure
• ✅ Implement robust 4-stage JSON parsing with LLM repair fallback — Created parse_json_response() with fallback chain: direct json.loads() → code block extraction → depth-based brace matching → LLM-powered repair. Added try_parse_json() and repair_json_with_llm() utilities in common/json_utils.py
• ✅ Add level/importance prioritization metadata to all report sections — Updated SUMMARY_PROMPT to include level (high=strategic, low=tactical) and importance (1-10) fields for tasks, problems, human_vs_ai, ai_limitations, learnings, conversation_summaries. Modified tool schema and increased max_tokens from 4096 to 8192

Implementation & Fixes

• ✅ Process historical conversation logs (2026-02-12 to 2026-02-16) — Ran summarize tool export phase on 5 dates worth of conversations across four devices; generated per-device/per-date JSON reports; merged cross-device summaries; published to Hugo website (136→141 pages on GitHub Pages)
• ✅ Initialize Motion-based Self-Reflection Framework documentation — Explored three-module architecture (MPM motion prediction via LLaVA, MCM motion correction, Diffusion Policy). Created CLAUDE.md documenting training/inference commands, Hydra config notes, dataset formats. User requested comprehensive bilingual README (Chinese-English mixed) covering all components including 11 deps/ submodules (openpi, GraspVLA, MimicGen, etc.). Launched 3 parallel exploration agents, designed ~1200-line documentation plan. CLAUDE.md completed, full README in progress.
• ✅ Clear MIHD affected vision and spatial coordinate caches — Deleted embeddings_cache/data/*/spatial_coords.npz (12 files) and entire embeddings_cache/vision/ directory containing embeddings based on incorrect coordinates
• ✅ Implement deploy subcommand for Hugo bugJournal batch publishing — Modified generate_hugo_post() to extract summary from report blockquote, fix timezone (-05:00), update keywords; created cmd_deploy() for batch processing with –date filter and automatic update.sh execution
• 🔄 Design checkpoint consolidation and policy visualization workflow — Inventoried 6 model checkpoints (BC-RNN 164MB, Pi0 series ~12GB each, robomimic 7.6MB, 47GB+ total). Designed checkpoints/ symlink structure and visualize_policy_rollout.py script combining rollout logic from 5_baseline_accuracy.py and rendering from 2_visualize_scene.py. Supports random/bc_rnn/vla policies with configurable seeds and camera views. Plan written but not implemented.
• ❌ Regenerate daily reports with corrected pricing (merge + deploy) — Attempted to regenerate markdown and JSON reports with corrected token costs using merge command, but encountered API connection failure (ConnectionRefused). Log export (data correction) completed successfully, but downstream report generation blocked by LLM API unavailability.

Problems & Solutions

Critical Issues

1. MIHD vision encoder single-modal ARI extremely low (~0.065), inconsistent with historical UNI2 performance; embeddings showed suspicious patterns (many duplicate rows initially)

Solution: Deep investigation of tissue_positions_list.csv revealed col4 is pxl_row_in_fullres (Y-axis) not pixel_x, col5 is pxl_col_in_fullres (X-axis) not pixel_y, but code naming was reversed. Confirmed by analyzing array_row/array_col step increments: col4 changes 119.8px/step (Y-axis), col5 changes 68.9px/step (X-axis). Fixed by correcting column names to (pxl_col, pxl_row) order

Key Insight: Data bugs are more insidious than model bugs: code runs normally, caches generate successfully, tests pass, but all results are based on wrong data. Requires tracing from macro-level anomalies (low ARI) back to raw data source (CSV column definitions). Spatial distance Euclidean invariance meant graph construction was unaffected, but vision patch content was completely misaligned

2. ccusage returns cost=0 for claude-opus-4-6, causing severe token cost tracking failure (Feb 17 actual ~$43 displayed as $0.90, 13x underestimation)

Solution: Implemented fallback pricing mechanism in daily_summary.py’s fetch_ccusage() function via _fix_zero_cost_models() post-processing logic. Added _FALLBACK_PRICING dictionary (opus-4-6: input $5/M, output $25/M, cache_creation $6.25/M, cache_read $0.50/M) to calculate correct costs when ccusage returns cost=0. Fixed field name bug (mb.get(‘model’) → mb.get(‘modelName’) or mb.get(‘model’)) for compatibility. Re-exported logs for Feb 13-17, correcting displayed costs from $3-7/day to actual $18-48/day.

Key Insight: Third-party tool pricing defects cannot be directly fixed—implement local fallback mechanism as practical solution. LiteLLM uses versioned model names (anthropic.claude-opus-4-6-v1 with -v1 suffix) while Claude Code logs use simplified names (claude-opus-4-6), causing systematic mismatch. Fallback design must trigger only on anomalous values (cost=0) to maintain backward compatibility when upstream fixes issue. Cost tracking bugs can cause 90%+ underestimation (13x in this case), severely impacting resource planning decisions. Always verify third-party tool output schema by printing raw data, not assuming from documentation.

3. tianhe non-impulse injectors (friction/pose_perturb/gripper_bias) crashed with ‘AttributeError: no attribute mj_name2id’ when trying to resolve body/geom IDs in MuJoCo model

Solution: Replaced direct mujoco.mj_name2id() calls with robosuite wrapper methods model.body_name2id() and model.geom_name2id(). Added _resolve_body_id() helper to try ’name’ first then ’name_main’ suffix (robosuite naming convention). Wrapped in try/except for graceful handling of missing bodies.

Key Insight: Robosuite wraps MuJoCo with its own API layer and adds _main suffix to object body names. Direct MuJoCo C API calls bypass the framework layer and break—must use framework-provided wrappers that handle name translation. Robot simulation frameworks layer abstractions over raw physics engines; direct API calls that bypass the framework break. Always examine working reference code (e.g., ImpulseInjector) in the same codebase before modifying.

4. tianhe BC-RNN policy crashes with KeyError ‘object’ because StateExtractor generates obs dict with ‘object-state’ key while robomimic training data used ‘object’ key (different robosuite tasks have inconsistent key names: PickPlace uses object-state, Lift uses object)

Solution: Added key mapping in _to_robosuite_obs() Strategy 1 to alias object-state → object before returning raw obs. Updated Strategy 3 fallback to concatenate pos+quat from StateExtractor’s objects dict into object key. Validated with 79 unit tests and GPU integration test confirming KeyError eliminated.

Key Insight: Robosuite automatically appends -state suffix to grouped modalities (e.g. object → object-state), but training data uses original names. Adapter layers must bridge this naming convention gap. Different robosuite tasks have inconsistent observation key naming conventions. Manual obs reconstruction cannot guarantee key name consistency with training time. Safest approach is passing original env.step() return value directly with key aliasing. This highlights that cross-task compatibility requires either (a) unified obs format or (b) explicit key remapping layer

5. LLM responses for structured reports often return malformed JSON: unescaped quotes, incomplete objects, extra text, or truncation near token limits, causing parse failures and workflow interruptions

Solution: Implemented 4-stage fallback parser: (1) direct json.loads(), (2) extract ```json blocks, (3) depth-based brace matching to find complete JSON object, (4) call LLM to repair malformed JSON. For APIs supporting it, use tool_use (Anthropic) or response_format=json_object (OpenAI) to enforce valid JSON

Key Insight: LLM-generated structured data requires defensive parsing with multiple fallback strategies. Native JSON mode (tool_use/json_object) significantly reduces but doesn’t eliminate failures. Including LLM-powered repair as final fallback creates robust pipeline. Each fallback layer handles specific failure mode: code blocks handle markdown formatting, brace matching handles truncation, repair handles syntax errors

6. BC-RNN model achieves 0% success rate despite KeyError being fixed—robot hovers over bin without approaching target can

Solution: Generated 3 visualization videos (400 frames each) showing robot behavior. Root cause confirmed as undertrained checkpoint (epoch 600 with limited demo data), not framework bug. Videos reveal robot hovers but never attempts grasping. Need to train for more epochs or use better checkpoint.

Key Insight: GPU integration tests are critical even when unit tests pass. BC-RNN passed all 79 unit tests but required actual GPU rollout to discover 0% SR issue. Video visualization is essential for diagnosing behavior quality issues in robotic policies. Framework correctness vs model quality require different validation strategies: unit/integration tests confirm code logic; video visualization reveals behavior quality.

7. SSH connections to tianhe HPC cluster frequently disconnect, causing Claude Code session loss and inability to resume work; VS Code Remote SSH similarly affected

Solution: Implemented layered defense-in-depth solution: (1) SSH keep-alive on both ends (15s heartbeat, 90s tolerance), (2) tmux session persistence so processes survive disconnects, (3) claude-tmux script for one-command session resume with auto proxy, (4) commented auto proxy in .bashrc to isolate user environment for shared accounts, (5) documented local client config for ControlMaster multiplexing and auto-ssh reconnection script.

Key Insight: Network instability requires redundant mitigation at multiple layers: protocol-level (TCP keep-alive), SSH-level (ControlMaster), application-level (tmux + auto-reconnect). No single layer suffices for production reliability. tmux decouples session state from connection state, enabling true resume capability. Session-scoped proxy configuration (not global bashrc) avoids affecting other shared-account users. When working on shared server accounts, every environment modification (PATH, proxy, aliases) must consider multi-user implications.

8. Multi-device daily report workflow needs synchronization mechanism—devices export independently but merge requires access to all devices’ exports, which may not be available on the merging device

Solution: Adopted two-tier rclone sync: export uploads to /logs/, merge uploads final reports to /reports/. Added merge –sync flag downloading all devices’ logs/ before LLM aggregation. Implemented _merged_devices tracking to skip re-summarizing already-processed devices

Key Insight: Distributed workflows require explicit synchronization points. Separating raw exports (logs/) from aggregated outputs (reports/) prevents circular dependencies. Idempotent design (skip already-processed) enables safe re-runs without expensive redundant API calls

9. Original single-phase daily report design assumed single-device usage; when user wanted multi-device aggregation, existing architecture couldn’t handle it

Solution: Complete redesign from single-phase (read conversations → call API → generate report) to two-phase (Phase 1: export conversations to JSON per device; Phase 2: merge all exports + call API for aggregated report). User explicitly requested this architecture change

Key Insight: User requirements evolve during development. Human (user) identified single-device limitation that AI didn’t anticipate in initial design. Two-phase architecture is more complex but necessary for actual use case. This shows Human systems thinking was superior in foreseeing distributed deployment constraints

10. Scene generation produced zero friction/gripper_bias scenes despite injectors being enabled (only pose_perturb worked initially)

Solution: Discovered detectors only generated friction/gripper_bias ErrorSpecs for specific subtypes that didn’t trigger during runs. Implemented _augment_specs_with_alternatives() method to programmatically generate friction/stuck and gripper_bias/stuck specs when original detectors produce no results. Generated 100 additional friction scenes (55 stuck, 28 tip_over, 17 large_offset), expanding database to 454 scenes.

Key Insight: Error scene generation is a funnel: detector triggering → ErrorSpec generation → injector application → validation → scene acceptance. Enabling an injector doesn’t guarantee scenes—depends on detector triggering frequency. Different error types have vastly different acceptance rates based on task dynamics. Pose perturbation is more universally applicable than friction/gripper anomalies in pick-place tasks. Programmatic spec augmentation can overcome detector coverage gaps.

11. No MimicGen pretrained BC-RNN checkpoints available for PickPlace task

Solution: Exhaustive search (official repo, HuggingFace, local filesystem) confirmed MimicGen only provides datasets, not pretrained model weights. Designed training-from-scratch plan using MimicGen-generated demo datasets with robomimic framework.

Key Insight: Official releases may provide data but not trained models. Always verify asset availability before planning dependent workflows. Training from demos became necessary fallback when pretrained checkpoints unavailable.

General Issues

12. Model checkpoints scattered across filesystem (BC-RNN under tangzijia/, VLA models under zhaoganlong/, 47GB total), hindering management and reproducibility

Solution: Designed checkpoints/ directory with symlinks to avoid copies: bc_rnn/pick_place → original path, vla/{pi0_libero, pi05_base, pi0_base, pi0_fast_base} → zhaoganlong cache. Updated benchmark_v4.yaml to use new centralized paths.

Key Insight: Large model assets (47GB+ VLA checkpoints) require symlink-based organization to avoid duplication while maintaining centralized discoverability. Scattered checkpoints across filesystem hinder reproducibility and config management.

13. Hugo front matter generation used hardcoded summary ‘AI Daily Summary’ and wrong timezone (+08:00 instead of -05:00), inconsistent with existing bugJournal entries

Solution: Modified generate_hugo_post() to extract actual summary from report’s blockquote (line 3, starts with ‘>’), changed timezone to -05:00, time to T00:00:00, removed ‘AI Daily Summary’ from keywords

Key Insight: Code generation should match existing content conventions, not impose new ones. Reading actual report structure (blockquote summary) is more maintainable than hardcoded strings

Human vs AI Approaches

Strategic Level

MIHD coordinate bug root cause diagnosis

Role	Approach
Human	User provided complete problem analysis and verification method: calculated array_row/array_col step increments’ pixel changes (Δcol4=119.8px, Δcol5=68.9px) to reverse-confirm column semantics—a data-feature-based verification method rather than relying on documentation or code comments. User demonstrated data scientist diagnostic thinking
AI	AI executed user-provided fix plan: modified column names, cleared caches, reran experiments, verified results (coordinate ranges, embedding uniqueness, ARI improvement). AI did not independently discover bug; implemented predefined solution

Difference Analysis: User demonstrated data scientist diagnostic thinking (using data itself to validate hypotheses); AI served as execution tool. Bug’s insidiousness lay in code logic being correct and caches generating normally—only external indicators (abnormally low ARI) or data feature analysis could reveal it. User’s domain intuition (’this doesn’t look right’) was critical trigger

Two-phase export/merge architecture for multi-device aggregation

Role	Approach
Human	User recognized original single-phase design couldn’t handle multi-device scenario and explicitly requested redesign: ‘I want export to be local-only, then merge pulls from all devices and calls API.’ User provided conceptual architecture
AI	AI initially designed single-phase solution (read → process → generate) assuming single-device usage. Only after user intervention did AI implement two-phase design

Difference Analysis: Human anticipated real-world deployment constraints (multiple devices, network-based sync) that AI missed in initial design. AI focused on immediate technical implementation without considering distributed usage patterns. Clear case where human systems thinking was superior

BC-RNN obs key mapping implementation approach

Role	Approach
Human	Provided complete implementation plan upfront with precise object-state → object mapping requirement, covering Strategy 1 (raw obs path) and Strategy 3 (StateExtractor fallback) with defensive programming. Anticipated the obs format mismatch from prior investigation and provided surgical fix.
AI	Executed implementation exactly as specified, validated with 79 unit tests, ran GPU integration test to confirm KeyError eliminated. Discovered 0% success rate requiring video analysis to distinguish framework bug vs model quality issue. Required visualization to diagnose root cause.

Difference Analysis: Human’s prior investigation enabled precise specification of fix location and logic. AI validated correctness via testing but needed visualization to diagnose model quality issue. Collaboration pattern: human provides architectural direction from domain knowledge, AI executes and validates with systematic testing. User has domain knowledge (robosuite task differences); AI lacks predictive capability for framework details, requiring multiple trial-error + user correction iterations to approach solution. Typical AI limitation in complex system integration

Scope prioritization for M5/M6 milestone completion under deadline pressure

Role	Approach
Human	Explicitly constrained scope: ‘defer multi-task expansion, use only pretrained models, skip missing VLA models.’ Pragmatically focused on proving evaluation pipeline works with minimal risk rather than maximizing coverage. Applied ‘done is better than perfect’ principle.
AI	Initially proposed comprehensive 6-phase plan including downloading MimicGen datasets, training BC-RNN from scratch, running full 4-policy evaluation, and creating comparison reports. Designed for technical completeness but potentially over-scoped for deadline context.

Difference Analysis: Human exhibited stronger project management instinct—recognizing that proving the system works with 2-3 policies is sufficient for paper writing milestone. AI leaned toward technical completeness without considering deadline pressure. Human’s pragmatic scoping prevented scope creep while maintaining milestone integrity.

ccusage pricing bug diagnosis strategy

Role	Approach
Human	Directly pointed to ‘ccusage cannot fetch LiteLLM’ as problem starting point, guiding AI to investigate LiteLLM pricing database and ccusage source code. Provided high-level problem localization from intuition.
AI	Launched multiple parallel Explore agents to systematically investigate ccusage integration architecture, log data structure, LiteLLM pricing JSON. Conducted broad evidence collection, verified hypothesis, and identified root cause (model name mismatch).

Difference Analysis: User drives requirement but abandoned at implementation planning stage, possibly because: (1) plan insufficiently specific, (2) wrong timing (already did too much work that day), (3) insufficient evaluation of existing tool modifications. AI failed to capture user intent change. Human provided intuitive direction based on domain knowledge. AI’s systematic exploration validated hypothesis and ensured no missing details. Human’s intuition guided search space; AI’s breadth search confirmed root cause definitively.

Historical data correction strategy for cost tracking bug

Role	Approach
Human	Explicitly required ‘run on all days to fix this bug’ and ‘fix the numbers in all the jsons and report mds’, emphasizing batch correction necessity from business perspective. Stressed data consistency across 5-day history.
AI	Executed daily export commands for data correction, discovered report files need separate processing (merge command calling LLM). Encountered API failure blocking report regeneration but completed log data correction successfully.

Difference Analysis: Human’s completeness requirement from business angle drove thorough fix vs just fixing current day. AI’s execution discovered technical dependencies (LLM API) causing partial blockage. Human emphasized data integrity; AI identified decoupling opportunity (separate export from merge).

SSH stability solution architecture for shared account environment

Role	Approach
Human	Specified two-part delivery: ‘On server, you handle it directly. For local PC, give me a document so local Claude can auto-execute.’ Separated concerns by control boundary and automation capability. Later specified conditional proxy: ‘only when I connect should settings activate, controllable via proxy_on/proxy_off.’
AI	Initially created unified plan for both server and local, restructured after user clarified. Initially designed auto-proxy in claude-tmux only, not realizing .bashrc line 136 would affect all users. After feedback, commented global auto-proxy and documented manual proxy_on workflow.

Difference Analysis: Human thought in terms of execution context (what I control vs. delegate) and shared resource implications (multi-user account). AI initially focused narrowly on ‘make it work’ without considering side effects on other users. Human’s infrastructure awareness prevented environmental pollution.

VLA config selection for robosuite environment compatibility

Role	Approach
Human	Immediately caught semantic mismatch: ‘Wait, don’t use droid config. This is robosuite environment, should use mimicgen version of pi0.5.’ Maintained context awareness about task-model compatibility constraints.
AI	Initially suggested testing with LIBERO and droid configs based on found checkpoint directories, without checking environment compatibility first. Exhibited premature optimization—found checkpoints and immediately proposed using them.

Difference Analysis: Human maintained stronger environment-model semantic compatibility awareness. AI exhibited ‘found tool, use tool’ pattern without validating applicability. Human’s domain knowledge prevented wasted effort on incompatible configs.

Daily report automation deployment need

Role	Approach
Human	User manually created multiple bugJournal entries then said ‘write a Python code to do this’—extracting automation need from repetitive labor. Specified requirements: given template, auto-convert md, copy to configurable location, run update.sh
AI	AI entered Plan Mode designing deploy subcommand, identified existing generate_hugo_post() limitations (hardcoded summary, single-date only), designed batch processing + smart summary extraction. But user rejected at ExitPlanMode

Difference Analysis: User drove requirement but abandoned at implementation planning stage, possibly because: (1) plan insufficiently specific, (2) wrong timing (already did too much work that day), (3) insufficient evaluation of existing tool modifications. AI failed to capture user intent change

JSON repair strategy selection

Role	Approach
Human	User accepted AI’s proposed 4-stage fallback but didn’t specify approach—trusted AI’s technical judgment on parsing strategies
AI	AI autonomously designed multi-stage fallback: direct parse → code block extraction → brace matching → LLM repair. AI also added tool_use/json_object mode suggestions for supporting APIs

Difference Analysis: Here AI had superior technical knowledge of JSON parsing failure modes and fallback strategies. Human provided requirement (‘fix JSON parsing’), AI provided solution architecture. Collaborative: human sets requirements, AI provides technical solutions

conversation_summaries feature requirement

Role	Approach
Human	User explicitly requested section to ‘summarize the conversation with AI’ at session level, recognizing that task lists don’t capture what was actually discussed
AI	AI proposed technical schema (JSON structure, markdown rendering, prompt modifications) but didn’t anticipate user need for narrative summaries

Difference Analysis: User drives product vision (what users need), AI provides implementation design (how to build it). Feature gap was user insight, not AI suggestion. Shows Human product sense superior to AI’s

AI Limitations

Critical Limitations

• Did not independently diagnose MIHD coordinate bug root cause; completely relied on user-provided analysis and fix plan. Faced with ‘abnormally low ARI’ macro indicator, AI lacked capability for autonomous data source tracing (from results → embeddings → patch extraction → coordinates → CSV column definitions backward chain)
• tianhe BC-RNN integration went through three failed fix attempts across two sessions; AI didn’t foresee robosuite different tasks (PickPlace vs Lift) having inconsistent observation key naming, nor realize at first fix that StateExtractor-rebuilt obs cannot guarantee key name consistency. Required multiple user corrections to approach correct solution. Used deprecated MuJoCo C API (mujoco.mj_name2id) instead of checking robosuite wrapper API first, causing immediate crashes in three injectors. Should have examined existing working code (ImpulseInjector) to understand correct API pattern before modifying other injectors.
• Did not anticipate multi-device distributed usage pattern in initial daily report tool design—assumed single-device workflow and required user to request two-phase architecture redesign
• When generating JSON near token limits, AI may truncate output mid-structure, producing malformed JSON requiring defensive parsing and repair logic in production systems
• ccusage fix initial implementation incorrectly used mb.get(‘model’) field when actual JSON had modelName, without first verifying third-party tool output structure. Caused fix to be completely ineffective until human discovered numbers still wrong and re-investigated. Also launched excessive parallel exploration tasks with redundant information collection (reading same log files multiple times).
• Initially proposed testing VLA models with LIBERO/droid configs without verifying environment compatibility. Failed to maintain semantic context that target environment is robosuite/mimicgen, not LIBERO. Required human correction to redirect toward appropriate configs.
• Cross-session memory deficit: AI in 2026-02-16 tianhe session couldn’t autonomously recall VLA checkpoint downloads from previous session, requiring user prompt to search broader server paths
• Cannot proactively anticipate user needs for features like conversation summaries—these product insights come from user observation of gaps in generated reports
• Struggles with deeply nested meta-tasks: when conversations contain conversations about conversations, AI has difficulty determining which level to analyze without explicit guidance about task scope and expected output
• When generating 2026-02-17 daily report, AI as daily report analyst itself became observation object—‘observer is observed’ meta-level complexity exists. AI needed to analyze own behavior that day (behavior of generating daily reports), requiring certain self-awareness capability

General Limitations

• Daily report automation deployment Plan Mode design was rejected by user; AI failed to capture user intent change or assess whether implementation plan specificity met user expectations, directly proceeding to ExitPlanMode rather than first asking for user feedback. Multiple failed ExitPlanMode attempts indicate difficulty judging when plan refinement is complete vs when user wants to continue iteration. User rejected plan exits 3 times across sessions, suggesting AI should ask ‘is this plan ready?’ rather than assuming completion.
• Cannot directly analyze video content to assess policy quality. BC-RNN rollout videos required manual keyframe extraction and human inspection to identify robot hovering behavior (ignoring grasp target). Could not infer 0% success rate indicated model quality issue without visual confirmation.
• When encountering API ConnectionRefused error during report regeneration, did not proactively propose fallback solution (complete data correction first, handle report generation separately later). Insufficient flexibility in workflow decoupling.
• Created long unified plan documents mixing server-side and local-client instructions without considering execution boundaries. Required user feedback to restructure into actionable separate deliverables. Weak project decomposition instinct.
• When user asked ‘Where are we now?’ (in Chinese), launched exploratory agents and read large documents instead of first checking for recent session context or TODO tracking systems. Over-relied on file reading when conversational context might have been sufficient.

Learnings

Key Learnings

• Data bugs are more insidious and destructive than model bugs: MIHD coordinate swap bug invalidated all vision experiment results, yet code logic was completely normal and all tests passed. Requires tracing from macro-level indicator anomalies (ARI) back to data source (CSV column definitions). This demands complete data scientist thinking chain; unit testing alone cannot discover such bugs
• Cross-framework integration naming convention differences are hidden traps: robosuite’s _main suffix, different tasks’ observation key name differences (object vs object-state), LiteLLM’s model name -v1 suffix—these details aren’t prominent in documentation but cause runtime errors. Robot simulation frameworks layer abstractions over raw physics engines (robosuite wraps MuJoCo). Direct API calls that bypass the framework layer break—must use framework-provided wrappers that handle naming conventions (e.g., _main suffix) and state management. Robosuite’s observation key naming convention: environment auto-appends -state suffix to grouped modalities (e.g. object → object-state), but training data often uses original names. Adapter layers must handle this mismatch. Safest strategies: (1) use framework-wrapped APIs not low-level APIs, (2) pass original data structures not manual reconstruction, (3) print actual output to verify field names not infer from docs. Always examine working reference code in the same codebase before modifying.
• Framework correctness vs model quality require different validation strategies. Unit/integration tests confirm code logic and eliminate errors like KeyError. Video visualization reveals behavior quality issues (robot hovering vs grasping). GPU integration tests are critical even when unit tests pass—BC-RNN passed 79 unit tests but required GPU rollout to discover 0% SR and visualization to diagnose undertrained checkpoint. Each validation layer serves different purpose.
• Multi-device collaboration daily report tool closed loop established: each device exports local logs → central hub merges remote logs → unified deployment to Hugo → GitHub Pages publishing. This workflow was intensively used on 2026-02-17 (processing 4 historical dates), proving two-phase architecture feasibility. Next step is automation (user already requested but not implemented). Meta-tooling (tools for analyzing tool usage) requires careful thought about workflow distribution—what happens on each device, how data synchronizes, how to handle partial information. Two-phase architectures (local export + centralized aggregation) are more robust than monolithic single-phase designs for distributed scenarios
• Error Recovery Benchmark scene library expansion from 251 to 454 is successful case of incremental extension: first implement impulse injectors (M4) → discover need for non-impulse types (M5) → fix MuJoCo API compatibility → generate multi-type scenes via detector candidate expansion mechanism. Demonstrates iteration path from minimum viable product (MVP) to feature-complete. Error scene generation is a funnel: detector triggering → ErrorSpec generation → injector application → validation → scene acceptance. Enabling an injector doesn’t guarantee scenes—depends on detector triggering frequency. Different error types have vastly different acceptance rates based on task dynamics. Pose perturbation is more universally applicable than friction/gripper anomalies in pick-place tasks. Programmatic spec augmentation (_augment_specs_with_alternatives()) can overcome detector coverage gaps.
• Third-party tool integration defensive programming: patch cost==0 (ccusage), field name compatibility (modelName or model), self-degradation for future fixes (when LiteLLM fixes, fallback becomes no-op). This ‘activate only when necessary’ patch strategy minimizes maintenance cost. Third-party tool pricing defects should be solved via local fallback mechanism rather than waiting for upstream fix. Design fallback to trigger only on anomalous values (cost=0) for backward compatibility. LiteLLM uses versioned model names (anthropic.{model}-v1 with optional :0 suffix) while Claude Code logs use simplified names, causing systematic mismatch. Cost tracking bugs can cause 90%+ underestimation (13x observed), severely impacting financial planning. Periodic audit of cost metrics critical.
• Human-AI collaboration works best when humans specify system architecture and strategic decisions while AI handles technical implementation and optimization. In this session, user’s architectural insights (two-phase design, multi-device sync) were critical; AI’s technical execution (parsing fallbacks, config optimization) filled gaps. Domain expert qualitative judgment is critical for discovering systemic problems. AI’s data-driven analysis and human experiential intuition form effective complementarity
• Remote development over unstable connections requires defense in depth across multiple layers: connection keep-alive (prevent), session persistence (survive), multiplexing (fast reconnect), and automation scripts (recover). Single-layer solutions fail under real-world network conditions. For shared server accounts, user-specific customizations belong in conditional blocks or separate launcher scripts, not universal .bashrc entries affecting everyone. tmux serves dual role as both state persistence and environment boundary. Session-scoped proxy configuration (not global bashrc) avoids affecting other shared-account users.
• LLM-generated structured data requires defensive parsing with multiple fallback strategies. Native JSON modes (tool_use for Anthropic, json_object for OpenAI) help but don’t eliminate failures—always implement repair as final fallback. Each fallback layer handles specific failure mode: code blocks handle markdown formatting, brace matching handles truncation, repair handles syntax errors
• Daily summary tools benefit from incremental schema evolution: start with basic task/problem lists, add session summaries for context, add prioritization metadata for filtering. Each layer serves different reader need. Batch documentation workflows benefit from two-phase architecture: (1) per-source extraction with local context, (2) cross-source consolidation with global deduplication. Pattern appears in summarize tool (export→merge), MIHD pipeline (Phase 1 cache embeddings→Phase 2 fuse), and error-recovery-benchmark (generate scenes→collect data)
• MimicGen release policy: only datasets provided, no pretrained model checkpoints. Official releases may provide data but not trained models—always verify asset availability before planning. BC-RNN (Behavioral Cloning with RNN) is straightforward supervised learning: collect human demo (state, action) pairs, train LSTM to predict actions, deploy as policy. Simple but effective baseline for manipulation tasks when sufficient demos exist. Training from demos became necessary fallback when pretrained checkpoints unavailable.
• Retroactive work log analysis (summarizing past week) is valuable practice turning raw conversation logs into structured knowledge artifacts, enabling pattern recognition and progress tracking across long timescales
• Idempotent design patterns (tracking what’s already processed, skipping redundant work) are essential for workflows that may be interrupted or re-run. The _merged_devices tracking prevented expensive redundant API calls when re-processing partially completed aggregations
• Large model asset management (47GB+ VLA checkpoints) requires symlink-based organization to avoid duplication while maintaining centralized discoverability. Scattered checkpoints across filesystem hinder reproducibility and config management. Centralized checkpoint directory improves discoverability.
• When fixing data quality bugs in historical data, decouple data correction (export) from downstream consumption (report generation). Avoid blocking entire pipeline due to downstream dependency failures (e.g., LLM API unavailability). Export phase depends on Node.js tools (ccusage), merge phase depends on LLM API—can proceed with export even if merge is blocked.

Practical Learnings

• Complex research project documentation scope should cover not just main modules but also deps/ submodules (11 found in Motion framework), data conversion pipelines, and cross-repo integration points (LeRobot, OpenPI, MimicGen). Thorough exploration prevents incomplete documentation that hinders onboarding.

Conversation Summaries

MIHD

🔄 Fix vision encoder X/Y coordinate swap bug and prepare experiment reruns 18:50:27.298 | claude_code User discovered UNI2 clustering results abnormal (151670 only 1 cluster, ARI≈0.065). Deep debugging revealed two serious bugs: (1) coordinate space mismatch (hires vs full-res), (2) X/Y coordinate swap (CSV col4=Y/col5=X but code naming reversed). Confirmed root cause by analyzing array_row/array_col vs pixel coordinate correlations. Fixed load_spatial_coordinates() and VisionEncoder coordinate handling logic, wrote execution plan for rerunning 66 single-modal experiments (3 gene encoders + 3 vision encoders × 11 sections), but user interrupted at plan mode exit.

🔄 Complete coordinate bug fix and validate through experiment reruns 21:28:38.729 | claude_code Fixed load_spatial_coordinates() CSV column mapping error (pixel_x was actually Y/pxl_row, pixel_y was X/pxl_col) causing all vision patches extracted from transposed positions, vision ARI only 0.065. Modified scripts/run_benchmark.py and utils/data_loader.py column names and coordinate order, cleared all spatial_coords and vision caches, re-extracted 11 sections’ UNI2/HIPT/ResNet50 embeddings, verified coordinates correct and embeddings 100% unique, UNI2 ARI improved to 0.12-0.25. Launched core_multimodal_fast fusion experiment reruns (STAIG UNI extraction completed, evaluation in progress).

gadget-summarize

✅ Generate consolidated 2026-02-13 report spanning 4 devices 13:56:41.357 | claude_code Large-scale consolidation of 2026-02-13 work across DCC (MIHD 286-experiment benchmark automation), tianhe (error recovery code cleanup v4.0→v4.1, GPU smoke test), MacBook (daily report tool creation with two-phase architecture), and Windows (CalendarPro P0 fixes). AI spawned general-purpose agent to handle complex multi-device aggregation.

✅ Implement deploy subcommand for Hugo bugJournal batch publishing 14:52:54.146 | claude_code User requested batch deployment feature for daily reports to Hugo website. AI implemented three changes: fixed generate_hugo_post() (extract summary from blockquote, fix timezone/time, update keywords), added cmd_deploy() for batch processing with –date filter, wired argparse dispatcher. Verification showed deploy –help working correctly.

🔄 Process Feb 15/16 date reports and deploy to Hugo, plan automation 03:57:32.066 | claude_code User requested processing 2026-02-15 and 2026-02-16 daily reports. Executed export –summarize (15th) → merge –sync (15/16th from 4 devices) → create Hugo bugJournal entries → run update.sh deploy (site 139→140/141 pages). Found additional 02-07/08/09/17 reports after rclone sync, created bugJournal entries for each. User requested Python script to automate this flow (template conversion + copy + update.sh), AI entered Plan Mode designing deploy subcommand but user interrupted.

✅ Configure local SSH client for stable tianhe connection (TzJsDesktop) 05:12:10.522 | claude_code User received server-side SSH stability guide from tianhe session and requested local-side configuration. AI updated ~/.ssh/config with keep-alive and connection multiplexing, created ~/bin/auto-ssh wrapper script with retry logic, added ~/bin to PATH via new .bashrc file. Complete setup for auto-reconnecting SSH with tmux session persistence.

Error Recovery Benchmark

✅ Fix ccusage opus-4-6 zero-cost bug and apply to historical dates 16:31:06.227 | claude_code Found ccusage reports cost=$0 for claude-opus-4-6 (actual 2026-02-17 25M+ tokens should be ~$21) due to LiteLLM database using anthropic.claude-opus-4-6-v1 (with -v1 suffix) causing match failure. Added _FALLBACK_PRICING and _fix_zero_cost_models() post-processing in daily_summary.py. Initial implementation incorrectly used ‘model’ field (actually ‘modelName’), completely ineffective. Fixed after printing raw JSON. Re-exported 2026-02-13 through 2026-02-17 five dates, 2026-02-14 cost correctly updated from $3.14 to $39.07.

✅ M5 milestone completion: non-impulse scene generation with multiple injectors 2026-02-17 | claude_code Implemented full pipeline for M5 non-impulse scene generation. Created benchmark_v4_nonimpulse.yaml config, fixed MuJoCo API bugs in three injectors (pose_perturb, friction, gripper_bias) by replacing direct mj_name2id calls with robosuite wrapper methods and adding _main suffix handling. Initial run generated 103 pose_perturb scenes but zero friction/gripper_bias scenes. Implemented _augment_specs_with_alternatives() method to programmatically generate specs when detectors don’t trigger. Final database: 454 scenes (121 impulse + 130 natural + 103 pose_perturb + 100 friction), achieving 227% of M5 target.

🔄 Advance tianhe M5/M6 milestones (SSH, non-impulse scenes, BC-RNN, VLA) 21:47:07.960 | claude_code Implemented SSH stability (tmux + keep-alive + claude-tmux script + session-scoped proxy). Fixed friction/pose_perturb/gripper_bias injectors’ MuJoCo API compatibility (mj_name2id→body_name2id + _main suffix handling), generated 103 pose_perturb + 100 friction scenes (database 251→454). Trained BC-RNN 600 epochs (15 min/A800). Fixed VLA server openpi API calls and image key mapping (Pi0/Pi0.5 dual servers ready). Debugged M6 multi-policy evaluation—Random working but BC-RNN crashes due to obs format incompatibility (object vs object-state key names + dimension 37≠65), still being fixed.

✅ BC-RNN integration: training, obs key mapping fix, and multi-policy evaluation setup 2026-02-17 | claude_code Comprehensive BC-RNN integration workflow. Searched for MimicGen pretrained checkpoints, confirmed none exist (only datasets). Trained BC-RNN from scratch using robomimic (2-layer LSTM, GMM head, 600 epochs on pick_place dataset, 14.3MB checkpoint). Fixed obs key mapping bug: implemented object-state → object aliasing in policy_adapter.py for both Strategy 1 (raw obs) and Strategy 3 (StateExtractor fallback). Validated with 79 unit tests and GPU integration test (5 rollouts, KeyError eliminated but 0% SR). Generated visualization videos revealing robot hovering behavior, confirming undertrained checkpoint not framework bug. Updated project docs to v4.6. Deployed VLA servers (Pi0 port 5556 GPU 1, Pi0.5 port 5557 GPU 3). Created scripts/5_baseline_accuracy.py for zero-error baseline testing. Started multi-policy evaluation run (50 scenes × 3 seeds × 4 policies).

✅ SSH connection stability solution for remote development over jump host 2026-02-17 | claude_code Diagnosed unstable proxy-based SSH connection causing Claude Code session loss. Designed and implemented five-layer solution: (1) SSH keep-alive config (ServerAliveInterval 15s), (2) enhanced .tmux.conf with history and stability settings, (3) ~/bin/claude-tmux launcher script with auto proxy_on, (4) commented global auto-proxy in .bashrc for user isolation on shared account (manual proxy_on/proxy_off), (5) created docs/local_ssh_setup_guide.md with client-side ControlMaster, auto-ssh script, and VS Code settings. All components verified working. Established stable remote development workflow preventing session loss.

🔄 Model checkpoint consolidation and visualization workflow design 2026-02-17 | claude_code Inventoried 6 model checkpoints: BC-RNN PickPlace (164MB, 12 epochs), Pi0 LIBERO (12GB), Pi0.5 Base (12GB), Pi0 Base (12GB), Pi0 Fast Base (11GB), Robomimic Lift (7.6MB). Total 47GB+. Discovered existing scripts only handle error scene replay, not live policy rollout visualization. Designed checkpoints/ symlink structure to avoid duplication and visualize_policy_rollout.py script combining rollout logic from 5_baseline_accuracy.py and rendering from 2_visualize_scene.py. Supports random/bc_rnn/vla policies with configurable seeds and camera views. Plan written but implementation not completed.

gadget (summarize tool)

🔄 Diagnose and fix ccusage opus-4-6 pricing bug revealing 13x cost underestimation 2026-02-17 | claude_code Human reported ccusage returning cost=0 for claude-opus-4-6 model, causing token cost tracking failure. AI launched parallel exploration of ccusage source, LiteLLM pricing database, and actual log data. Confirmed model name mismatch: Claude Code uses ‘claude-opus-4-6’ while LiteLLM database uses ‘anthropic.claude-opus-4-6-v1’. Implemented fallback pricing mechanism in daily_summary.py: added _FALLBACK_PRICING dictionary (opus-4-6: input $5/M, output $25/M, cache_creation $6.25/M, cache_read $0.50/M) and _fix_zero_cost_models() post-processing function. First implementation failed due to field name bug (used ‘model’ but ccusage outputs ‘modelName’). Fixed to mb.get(‘modelName’) or mb.get(‘model’). Re-exported logs for Feb 13-17 (5 days). Correction revealed actual costs $18-48/day vs displayed $0-7/day (13x underestimation, ~$150+ cumulative error). Attempted report regeneration (merge + deploy) but blocked by LLM API connection failure. Data correction completed, report generation pending API availability.

gadget (daily report system)

✅ Daily report consolidation for Feb 13-16 multi-device work 2026-02-17 | claude_code User invoked daily report analyzer on conversation logs from Feb 13-16, covering multiple projects across devices: CalendarPro (JSON parsing, AI timeout handling, message queue stability, learning data collection, Random Thoughts feature on Feb 13), MIHD benchmark optimization + Error Recovery framework (visualization fixes, force injection debugging, PreGrasp detector on Feb 14), Error Recovery VLA work (Pi0/MimicGen checkpoint research, Gemini VLM integration, 50-rollout natural error capture generating 150 scenarios on Feb 16). System processed all conversations and generated structured JSON reports with tasks, problems/solutions, human_vs_ai analyses, and conversation summaries for knowledge management and cross-device progress tracking.

Motion-based Self-Reflection Framework

🔄 Initialize comprehensive bilingual documentation (CLAUDE.md + README/Tutorial) 2026-02-17 | claude_code Executed /init command, explored three-module architecture: MPM (motion prediction via LLaVA), MCM (motion correction), Diffusion Policy. Created CLAUDE.md documenting training/inference commands, Hydra configs, dataset formats. User requested detailed bilingual README (Chinese-English mixed) covering all components including 11 deps/ submodules (openpi, GraspVLA, MimicGen, LeRobot, robosuite, robomimic, etc.). Launched 3 parallel exploration agents to analyze diffusion_policy/, llava/, failure_case_analysis/, and full deps/ tree. Designed ~1200-line documentation plan covering architecture, data conversion pipelines (RLDS → LeRobot → robomimic), training workflows (three-stage: LLaVA finetuning, MCM training, Diffusion Policy), and all cross-repo integration points. CLAUDE.md completed, comprehensive README document not written.

CalendarPro

✅ Fix Google Calendar 403 + Batch Delete Feature 00:50:22.155 | claude_code Fixed calendar_service.py to use settings().get() API instead of calendarList().get() (avoiding 403 insufficientPermissions). Implemented batch delete: extended DeleteData model with date_from/date_until/batch fields, updated LLM prompt, made search_events support custom time ranges, rewrote _handle_delete for batch operations, added BatchDeleteApprovalView Discord confirmation UI. Fixed LLM timeout fallback with regex extraction and empty message Discord crash.

Token Usage