Daily Log — 2026-03-22

Today’s Overview

  • What I did: Both devices running in parallel all day: tianhe focused on systematic debugging of the Error Recovery Benchmark (gripper polarity fix, error skill injection logic rewrite, three_piece_assembly regression root-cause analysis, attribution of 48 ungenerable cases); TzJsDesktop completed batch archiving of historical weekly reports, fixed the gadget website deployment pipeline, redesigned Hugo site navigation, and landed the unified deploy staging architecture.
  • How I did it: Used diagnostic scripts to measure gripper qpos behavior and pinpoint the polarity bug; implemented a conditional branch fix based on robot arm type; launched a Slurm 96-worker job to regenerate and validate the fixes; used escalated permissions to trace through the code chain and analyze three_piece_assembly regression; batch-processed daily report JSON files with the weekly report tool; implemented a dropdown menu via a custom Hugo partial and CSS; created common/site_staging.py to unify the output interface.
  • Why it matters: Error Recovery Benchmark training scenes increased to 1627 (+35%), covering 148 subtypes; the root cause of three_piece_assembly regression is clearly identified, providing precise code location for follow-up fixes; 8 historical weekly reports systematically filled in coverage for February–March; the gadget website deployment pipeline is stable, Hugo site navigation is clean, and the unified deploy pipeline is fully landed.

TzJsDesktop

  • What I did: Batch-generated W05–W12 historical weekly reports, fixed the gadget website deployment pipeline, completed the Hugo site bugJournal dropdown menu redesign, and implemented the unified deploy staging architecture.
  • How I did it: Called the gadget summarize weekly report tool to process multi-week daily report data; used git operations to fix the PaperMod theme (deleted ._pack files, updated theme, resolved merge conflicts); fixed two robustness issues in update.sh; iteratively fixed Hugo template bugs in Codex sessions; created common/site_staging.py and website/sync_staging.py.
  • Why it matters: Historical weekly reports now cover 2026 W05–W12; website deployment pipeline restored to stable; Hugo site navigation is clean, and all tool publish paths are unified under outputs/site.

tianhe

  • What I did: Completed three core bug fixes for the Error Recovery Benchmark (gripper polarity, wrong_object, drop_with_interaction), analyzed the root cause of three_piece_assembly phase detection regression, and systematically attributed 48 ungenerable cases.
  • How I did it: Wrote a diagnostic script to measure gripper qpos; used an if-else fix for polarity based on robot arm type; submitted Slurm job 50080 (96 workers) to regenerate and validate; used escalated permissions to bypass bwrap sandbox restrictions to read the code chain; read summary.json/meta_partial/parallel_logs to analyze failure causes.
  • Why it matters: The three bug fixes passed 139 unit tests; training scenes increased from 1209 to 1627; the root cause of three_piece_assembly regression (both get_target_object and InteractionSegmenter select targets by nearest distance from all_objects, so the base fixture with z≈0.80 never satisfies lift_height=0.84) is clearly identified; 48 ungenerable cases confirmed as physical constraints rather than pipeline failures.

tianhe cluster completed three core bug fixes for the Error Recovery Benchmark (gripper polarity reversal, wrong_object filtering logic, drop_with_interaction injection strategy), growing training scenes from 1209 to 1627 (+35%), with deep root-cause analysis of three_piece_assembly phase detection regression and physical constraint attribution for 48 ungenerable cases; TzJsDesktop batch-archived W05–W12 historical weekly reports, fixed the gadget website deployment pipeline, and completed Hugo site navigation dropdown menu redesign and unified deploy staging architecture.

Today’s Tasks

Architecture & Strategy

  • ✅ Error Recovery Benchmark: three core bug fixes — gripper polarity, wrong_object, drop_with_interaction — Fixed three core bugs: (1) Panda gripper action polarity reversal — dynamically detect polarity based on robot arm type in env_wrapper.py, add get_gripper_action_close/open() helpers, replace all hardcoded action[-1] across 9 error skills; (2) wrong_object filtering — only select graspable objects with non-empty grasp_geoms, excluding fixed fixtures such as the coffee machine; (3) drop_with_interaction rewrite — transport the object to directly above the non-target object (+0.15m) and release, tracking object-object contact throughout the settle process. Passed 139 unit tests after the fixes; submitted Slurm job 50080 (96 workers) for validation.
  • ✅ three_piece_assembly phase detection regression root-cause analysis — Confirmed Fix1–3 are landed; identified two remaining unfixed root causes: both get_target_object() and InteractionSegmenter select targets by nearest distance from all_objects, so the base fixture (z≈0.80 is permanently fixed and never satisfies lift_height=0.84), causing phase_labels to all be pre_reach — 879 out of 887 opportunities filtered. Proposed fix: ‘prioritize _get_graspable_objects(), fall back to all objects only when necessary’; code changes not landed due to bwrap sandbox restrictions.
  • ✅ Unified deploy staging architecture — Created common/site_staging.py to unify the output interface; all tool Hugo deploy paths switched to outputs/site; created website/sync_staging.py for staging→website sync (symlink preferred, fallback to copy on failure, first-run bootstrap migrates historical content); benchmark now has a complete publish layer (publish.py + –deploy CLI flag + navigation entry); update.sh/update.ps1 integrate the staging sync step; sync.py sync source switched to outputs/site.
  • ✅ Systematic attribution of ungenerable cases (48 subtypes) — Attributed the 48 subtypes that still failed to reach 10/10 after a full re-run into 5 categories of physical constraints: collision_eef_object (non-target object displacement < 1cm threshold), position_error (D0/D2 injection magnitude insufficient), wrong_object (gripper not closed / EEF distance > 8cm), grasp series (grasp not established), drop series (real drop physical constraints). Confirmed as validation threshold issues rather than pipeline failures.
  • ✅ Hugo site bugJournal navigation dropdown menu — Overrode the PaperMod default template via a custom header.html partial, added has-submenu/submenu class rendering logic, implemented hover dropdown via bugjournal-menu.css; created a new Posts & Notes section; list.html filtering makes the bugJournal root page show only the three sub-section entries (Daily/Weekly/Monthly); legacy root-level files remain in place without migration.

Implementation & Fixes

  • ✅ gadget website deployment pipeline fix — Fixed PaperMod theme pack index corruption (deleted macOS ._pack* files), updated theme to the latest version (resolving missing get-page-images partial required by Hugo v0.157.0), resolved head.html merge conflict (kept local MathJax + checkbox scripts), fixed two robustness issues in update.sh (find cleanup error + empty commit error).
  • ✅ Training scene regeneration: 1209 → 1627 scenes — Submitted a 96-worker parallel generation job, covering 160 (task, subtype) pairs, completed in ~30 minutes. Total: 1627 scenes (+35%), covering 148 subtypes; only three_piece_assembly/collision_eef_object_D0 failed due to timeout.
  • ✅ 13 error skill implementations documented in project overview — Read all 13 error skill source files; updated the project overview document in table form (can_inject conditions, inject method parameters, validate criteria), including design pattern summary and training generation status.
  • ✅ Batch generation of historical weekly reports (W05–W12) — Based on structured daily report data, batch-generated 8 weekly report JSON files for 2026: W05 (2/1), W06 (2/2–8), W08 (2/16–22), W09 (2/23–3/1), W10 (3/2–8), W11 (3/9–15), W12 (3/16–22), covering key milestones including MIHD STAIG ARI fixes, Error Recovery Benchmark upgrade, RoboBrain pi0.5 task completion detection, CalendarPro, and other project progress.

Issues & Solutions

Critical Issues

1. Panda robot arm gripper action polarity is opposite to code assumption: Panda requires action=+1 to close, but the code hardcodes -1, which actually opens the gripper — causing all injections in coffee/stack/stack_three/threading to fail

Solution: In EnvWrapper.init, detect the robot arm type via env.robots[0].gripper.class.name: set close_action=+1.0 for PandaGripper, close_action=-1.0 for RethinkGripper; add get_gripper_action_close/open() helpers and replace all hardcoded action[-1] references.

Key Insight: The same comment ‘−1=open, +1=closed’ exists for both Panda and Sawyer, but their internal multiplier directions differ, so behavior is reversed. Comments alone cannot be trusted — a minimal diagnostic script to measure qpos changes is necessary.

2. three_piece_assembly scan count degraded from 23 to 4: 879 out of 887 opportunities have phase pre_reach and are filtered by valid_phases

Solution: Root cause identified: both get_target_object() and InteractionSegmenter select targets by nearest distance from all_objects; the base fixture (z≈0.80 is fixed and permanently fails lift_height=0.84). Fix: change both to ‘prioritize _get_graspable_objects(), fall back to all objects when necessary’; code changes pending next session due to sandbox restrictions.

Key Insight: In multi-object assembly tasks, fixtures will consistently win the ’nearest object’ competition; target_object selection must be based on grasp_geoms filtering rather than pure distance.

3. wrong_object generated 0 scenes in coffee/three_piece_assembly: the skill attempted to grasp fixed fixtures (e.g., the coffee machine) that have no grasp_geoms, so EEF cannot reach their center and the gripper cannot close

Solution: Filter non-target candidate objects to require them to be in the task config’s objects list and have non-empty grasp_geoms, excluding fixed fixtures.

Key Insight: Task configurations contain both graspable objects and fixed fixtures; skills must filter by grasp_geoms rather than treating all objects as valid wrong-target candidates.

4. drop_with_interaction generated 0 scenes across all 6 tasks: the original implementation only releases the object with a 1–3cm offset from the current position; with 10–20cm gaps between objects, neighboring objects are never reached

Solution: Rewrote the inject logic: transport the object to directly above the non-target object (+0.15m), then release the gripper so the object free-falls onto the non-target; also track object-object contact throughout the entire settle process in rollout_utils.py.

Key Insight: Making an object ‘interact with other objects’ requires actively transporting it to a collision position — relying on random motion after a small offset to happen to hit a neighbor is insufficient.

Solution: Switched to eq .Title “bugJournal” as the condition, completely avoiding Hugo’s inconsistent internal field case handling.

Key Insight: Hugo handles the case of .Section and .RelPermalink inconsistently across versions and platforms; .Title is the most reliable identifier for a specific section list page.

6. PaperMod theme header.html does not support nested submenu rendering

Solution: Created a custom header.html in website/layouts/partials/ to override the theme default template; added has-submenu/submenu class rendering logic; implemented hover dropdown via bugjournal-menu.css.

Key Insight: Hugo supports safely overriding theme partials via the project-level layouts/partials/ directory; the parent field in config.yml menus declares submenu relationships — no need to fork or modify theme source code.

7. The benchmark static directory static/benchmark/ conflicting with content page content/benchmark.md path prevented /benchmark/index.html from being generated; the wrapper frontmatter used datetime.now(), causing Hugo to treat it as future content and not publish it

Solution: Renamed the static report directory to benchmark-report/ and updated the wrapper page’s internal links accordingly; removed the dynamic timestamp and replaced it with a fixed past date.

Key Insight: When a Hugo static directory name matches the base name of a content page, the static directory overrides the content rendering result; auto-generated frontmatter should not use the system current time as the date field.

General Issues

8. PaperMod theme pack index corruption (._pack*.idx) causing git pull failure (non-monotonic index), and Hugo v0.157.0 requiring a new partial that the old theme lacked, causing build failure

Solution: Deleted macOS resource fork files (._pack*.idx and ._pack*.pack), then git stash + git pull origin master + git stash pop; updated the theme to the latest version and manually resolved the head.html merge conflict to retain local MathJax and checkbox scripts.

Key Insight: macOS-generated ._-prefixed resource fork files can corrupt the .git/objects/pack directory; after a major Hugo version upgrade, the theme must be updated in sync, and local customized layouts must be manually preserved during merging.

9. bwrap sandbox unavailable on tianhe nodes (Unknown option –argv0), causing ~10 tool call failures

Solution: Requested elevated permissions via sandbox_permissions=‘require_escalated’, bypassing the bwrap restriction — file reads then worked normally.

Key Insight: The default Codex environment sandbox is unavailable on certain HPC node configurations; escalated permissions are the only viable option. The apply_patch tool is similarly restricted.

10. In set -e mode, update.sh was terminated when find cleanup of public/ returned a non-zero exit code, and again when git commit returned exit code 1 on no-change runs

Solution: Added 2>/dev/null || true at the end of the find command; added a git diff –cached –quiet check before committing — gracefully skip the commit and push steps if there are no changes.

Key Insight: In shell scripts with set -e, all cleanup operations should be protected with || true; deployment scripts should exit gracefully rather than error on no-change runs (idempotent design).

Human Thinking vs. AI Thinking

Strategic Level

Gripper Diagnostic Direction and Fix Strategy

Role Approach
Human Started from physical intuition, noting the likely binary issue, explicitly requesting “if the two robot arms have different controls, write some if-else to distinguish them” — preference for concise, explicit conditional branches.
AI Dove deep into robosuite source code to analyze format conventions, tending to infer polarity automatically from gripper config — a more systematic but more roundabout approach; a diagnostic script was needed to reach a definitive conclusion.

Analysis: The user’s intuition was more concise and practical (quickly narrowing the problem space), with a preference for clear, readable conditional branches; AI tended toward systematic but verbose code analysis, preferring automatic inference over explicit branching.

drop_with_interaction Fix Approach

Role Approach
Human Directly gave the core insight: “It’s simpler — just move above that object and then drop” — directly pointing out the need to actively transport rather than relying on random offsets.
AI Original implementation only applied a 1–3cm offset at the current EEF position before releasing, relying on the object’s natural motion to hit a neighbor — too constrained by the existing injection framework, missing the core requirement of active approach.

Analysis: The user has a more intuitive spatial understanding of physical interaction; AI never questioned this fundamental design flaw across multiple rounds of fixes, only correcting it after the user explicitly pointed it out.

three_piece_assembly Regression Root-Cause Analysis

Role Approach
Human Had independently completed a full 5-step causal chain analysis before the session (including specific file line numbers L144–148/L230–231, NPZ evidence, 887/879 statistics), presenting the question with high information density.
AI Verified the causal chain step-by-step as provided by the user; additionally discovered that InteractionSegmenter has the same fixture-priority issue (user’s analysis only covered down to the env_wrapper layer); planned an implementation path.

Analysis: The user’s pre-analysis quality was very high and accurately identified the primary root cause; AI’s value was in cross-validating the existing analysis, finding an additional missed point (the InteractionSegmenter layer), and planning the implementation path — not in original discovery.

Unified Deploy Staging Architecture Concept

Role Approach
Human Proposed the core architecture: “put it in a shared folder”, “based on the current website folder structure”, “update.sh symlinks to the correct location before pushing”.
AI Handled implementation details: staging helper API design, link-or-copy fallback strategy (accounting for Windows symlink permission restrictions), first-run bootstrap migration logic, and other robustness considerations.

Analysis: High-level architectural direction was provided by the user; AI translated the concise description into concrete implementation and added edge-case handling such as Windows compatibility.

AI Limitations

Significant Limitations

  • When facing internal behavior of underlying libraries (robosuite), AI could not reach a definitive conclusion through static code analysis alone — a diagnostic script was required to confirm. The initial design of drop_with_interaction had a fundamental flaw (small offset rather than active transport) that AI never questioned across multiple rounds of fixes, correcting it only after the user directly pointed it out.
  • bwrap sandbox restrictions on tianhe nodes caused ~10 tool call failures; AI failed to recognize the need for escalated permissions earlier. The apply_patch tool is similarly restricted, and the complete code patch (dual fix for env_wrapper + InteractionSegmenter) was never landed — the conversation was interrupted at a critical point.
  • Insufficient mastery of Hugo template field semantics: section conditional filtering needed three iterations to converge; failed to anticipate the static directory vs. content path conflict; failed to anticipate the datetime.now() future-date filtering issue. All of these are errors that could have been avoided at the design stage.

General Limitations

  • AI tends to overestimate task complexity (listing too many allowedPrompts in ExitPlanMode was criticized by the user); multiple tool call failures due to ‘file not read first’; apply_patch line-level positioning was unstable, requiring multiple retries; rg command output exceeding 262144 tokens was truncated, requiring multiple sub-queries to piece together results.
  • During weekly report generation, W09 was generated twice (with highly overlapping content) — AI did not proactively identify and flag the duplication; when some daily report input formats were abnormal (unknown dates, single human_vs_ai entries mixed in), AI handled them compatibly without reporting the upstream data quality issue to the user.

Today’s Takeaways

Key Takeaways

  • PandaGripper and RethinkGripper in robosuite have opposite action polarities (Panda action=+1 closes, Sawyer action=−1 closes); code comments are unreliable and must be verified through actual qpos measurement. When diagnosing unknown behavior, writing a minimal measurement script is more reliable and efficient than static code analysis, especially for internal behavior of third-party libraries.
  • In multi-object assembly tasks, target_object selection must filter out fixtures based on grasp_geoms; a pure ’nearest object’ strategy will systematically select the wrong target when a fixture is near the EEF, causing phase_labels to all be pre_reach. Error skill non-target candidates must likewise distinguish graspable objects from fixed fixtures.
  • The outputs/site staging architecture pattern: tool writes to outputs/site → website/sync_staging.py syncs → Hugo build → deploy. This decoupling keeps each tool’s publish logic independent of the website directory structure; sync_staging acts as a single entry point for incremental updates.
  • In physical simulation, making an object ‘interact with other objects’ requires actively transporting it to a collision position — relying on natural motion after a random offset is insufficient (object spacing is typically much larger than the offset). Ungenerable cases caused by benchmark validation thresholds (collision detection 1cm displacement, position error 2cm) are physical constraint issues of insufficient injection strength — requiring adjustment of injection parameters or validation thresholds, not pipeline re-runs.
  • Hugo best practices: .Title is the most reliable field for identifying a specific section (.Section and .RelPermalink have inconsistent case handling across platforms); a static directory name matching a content page base name creates a path conflict (static files override content rendering); auto-generated frontmatter should not use the system current time; theme partials can be safely overridden via the project-level layouts/partials/ directory to implement dropdown menus — no need to fork the theme.

Practical Takeaways

  • macOS resource fork files (._-prefixed) can corrupt git pack directories, causing non-monotonic index errors; after major Hugo version upgrades, the theme must be updated in sync; in shell scripts with set -e, cleanup operations should be protected with 2>/dev/null || true; deployment scripts should gracefully skip commits when there are no changes (idempotent design).
  • Slurm ai partition must specify –gres=gpu:a800:[N] even for tasks that do not use GPU; MUJOCO_GL=disabled enables physical simulation without GPU rendering (CPU-only). Batch archiving strategy (generating multiple weeks of historical weekly reports in one go) is an efficient method for building a searchable knowledge base.

Session Summary

Error Recovery Benchmark

🔄 Three core bug fixes (gripper polarity, wrong_object, drop_with_interaction) + three_piece_assembly regression root-cause analysis + 48 ungenerable cases attribution 01:12:20.861 | claude_code + codex Full-day systematic debugging of training data generation quality: (1) statistics revealed drop_with_interaction all at 0 and all grasp-type Panda tasks failing; (2) diagnostic script confirmed Panda gripper polarity is reversed — modified env_wrapper.py and 9 error skills, submitted Slurm job 50080 for regeneration (1209→1627 scenes, +35%), passed 139 unit tests; (3) fixed wrong_object filtering logic (excluding fixtures with no grasp_geoms); (4) rewrote drop_with_interaction (actively transport to directly above non-target and release, tracking contact throughout); (5) documented all 13 error skill implementations in the project overview; (6) used escalated permissions for deep analysis of three_piece_assembly regression — 879/887 opportunities are pre_reach, root cause is fixture being misselected as target_object by both get_target_object() and InteractionSegmenter; code fix not landed due to sandbox restrictions; (7) after a full re-run, systematically attributed 48 ungenerable cases to 5 categories of physical constraint threshold issues, confirming non-pipeline failures.

gadget

✅ Batch archiving of historical weekly reports (W05–W12) + website deployment pipeline fix + Hugo site navigation redesign and unified deploy staging architecture 20:08:01.600 | claude_code + codex Three parallel workstreams: (1) batch-generated 8 historical weekly report JSON files for 2026 W05–W12, covering key milestones including MIHD STAIG ARI fixes (0.09→0.54), Error Recovery Benchmark v4→v5 upgrade, RoboBrain pi0.5 task completion detection head, CalendarPro context-aware intent classification, and other project progress; (2) fixed PaperMod theme pack index corruption, updated theme to latest version (resolving Hugo v0.157.0 missing partial), fixed two robustness issues in update.sh (find cleanup + empty commit) — website deployment pipeline restored to stable; (3) implemented bugJournal hover dropdown menu (custom header.html partial + CSS, converged after three iterations using .Title as the condition), created a new Posts & Notes section, established outputs/site unified staging area (common/site_staging.py + website/sync_staging.py), added complete benchmark publish layer, resolved static directory path conflict and future-date bug, integrated staging sync in update.sh, and final verification passed hugo build end-to-end.

Token Usage

Claude Code

Overview

Metric Value
Total Tokens 69,161,269
Input Tokens 135,208
Output Tokens 165,763
Cache Creation 4,592,748
Cache Read 64,267,550
Cache Hit Rate 93.3%
Total Cost (USD) $50.6294

Model Breakdown

Model Input Output Cache Creation Cache Read Cost Share
claude-opus-4-6 49,276 111,729 2,401,502 52,058,136 $44.0781 87.1%
claude-haiku-4-5-20251001 85,875 53,919 1,315,313 11,988,530 $3.1985 6.3%
claude-sonnet-4-6 57 115 875,933 220,884 $3.3529 6.6%

Usage by Device

Device Total Tokens Input Output Cost
tianhe 46,473,057 102,750 122,803 $30.2102
TzJsDesktop 22,688,212 32,458 42,960 $20.4192

Codex

Overview

Metric Value
Total Tokens 17,143,340
Input Tokens 17,039,449
Output Tokens 103,891
Reasoning Tokens 40,222
Cache Read 16,036,864
Total Cost (USD) $8.0740

Model Breakdown

Model Input Output Reasoning Cache Read Cost Share
gpt-5.4 17,039,449 103,891 40,222 16,036,864 $8.0740 100.0%