Daily Log — 2026-03-08

Today’s Overview

  • What was done: Three projects advanced in parallel across two machines. On tianhe: completed full removal of 65 symlinks and code path migration for error_recovery_benchmark (Phase 1–9), MP4 visualization for coffee/stack/three_piece_assembly error scenarios, BC-RNN performance analysis, pre-error replay storage layer development, VLA-RoboTwin critical region annotation system implementation, threshold calibration distance recording, and Vulkan rendering fix. On TzJsDesktop: added author search, from-overview initialization, deduplication early-stop, and other features to gadget research_scout.
  • How it was done: The error_recovery_benchmark refactor used 4 parallel sub-agents covering each migration phase, with the main thread handling Makefile/docs directly. Slurm srun drove three idle A800 GPUs on an53 to render MP4s in parallel. VLA-RoboTwin used a template method pattern via _base_task.py inheritance for the critical region annotation. On the gadget side, the author search branch mirrored the conference search architecture, and AskUserQuestion was used to proactively clarify requirement boundaries.
  • Why it matters: The error_recovery_benchmark codebase now has zero indirection layers (127 tests all green), the day produced 9+ error scenario MP4s, BC-RNN grasp-phase bottlenecks were exposed, and the pre-error storage layer design is complete. The VLA-RoboTwin dataset now carries per-frame distance observations, and the data collection script runs correctly on headless HPC nodes after the Vulkan fix. The gadget tool now supports tracking specific researchers’ latest work by author.

TzJsDesktop

  • What was done: Added author search (–author), from-overview project initialization, and search deduplication early-stop to gadget research_scout, and updated the CLAUDE.md documentation accordingly.
  • How it was done: Mirrored the conference search caching/CLI/function design, added search_arxiv_author() (au: query + optional keyword combination), and used AskUserQuestion to confirm requirements before planning and implementing.
  • Why it matters: The research tool now supports tracking papers by specific researchers and running a full two-stage LLM evaluation pipeline, significantly reducing manual search overhead.

tianhe

  • What was done: Led the full removal of 65 symlinks and code path migration for error_recovery_benchmark (Phase 1–9), error scenario visualization for coffee/stack/three_piece_assembly, pre-error trajectory replay storage layer extension, and VLA-RoboTwin critical region annotation system implementation, threshold calibration distance recording, and Vulkan rendering fix.
  • How it was done: 4 parallel Claude sub-agents covered each migration phase while the main thread handled Makefile/docs. Slurm srun drove three idle A800 GPUs on an53 to render MP4s in parallel. VLA-RoboTwin used the _base_task.py template method pattern, with subclasses overriding distance calculation logic by task type.
  • Why it matters: Codebase path consistency is now 100%, all 127 unit tests pass, 9+ MP4s are written to disk, and BC-RNN failures are concentrated in the grasp phase. The VLA dataset carries per-frame ee_target_distances, and the data collection script runs normally on headless nodes after the Vulkan fix.

Completed a large-scale error_recovery_benchmark refactor on the tianhe server (full cleanup of 65 symlinks + code path migration), developed multi-task error scenario MP4 visualization and a pre-error replay storage layer, advanced the VLA-RoboTwin critical region annotation system and Vulkan rendering fix, and locally added author search and several other features to gadget research_scout.

Today’s Tasks

Architecture & Strategy

  • ✅ error_recovery_benchmark: Remove all 65 symlinks and complete code path migration (Phase 1–9) — Followed a 9-phase plan to change all error_framework imports to error_benchmark.framework, updating script_utils, YAML configs, argparse defaults, checkpoint paths, Makefile, Phoenix/FLARE scripts, documentation, and sys.path parent chain depths, then deleted all 65 symlinks. All 127 unit tests pass. Planning completed in session 3 (01:17); implementation completed in session 2 (01:40) via 4 parallel sub-agents. AI also fixed 2 unplanned sys.path parent chain depth errors.
  • ✅ VLA-RoboTwin: Critical region annotation system implementation and threshold calibration distance recording (10 tasks) — Added per-frame critical_region binary labels (τ=0.10m, three strategies: static target / grasp / place-and-stack) for 10 robot manipulation tasks, in preparation for pi0.5 CLS token classifier training data. Since the collected data had critical_region all zeros (threshold estimated too small), further added get_ee_target_distances() for each task (4–8 distance variables per task) to record raw distances for threshold calibration.
  • 🔄 error_recovery_benchmark: Add pre-error context replay to visualization (from 5s before error or action start) — User requested that videos replay from 5 seconds before the error or from the very beginning of the action, rather than from a neutral frame. Completed: added action_history collection and initial_state saving in rollout_generator, extended _save_scenes NPZ storage, and extended load_policy_scene_state loading logic. The actual replay logic in the visualization script was not completed due to context exhaustion.
  • ✅ gadget Research Scout: Author search, from-overview initialization, deduplication early-stop, and other feature additions — Added –author parameter (search_arxiv_author() with au: exact query + optional keyword combination + cache support + –conference mutual exclusion check, fully wired for both search-only and two-stage LLM evaluation modes); init –from-overview (LLM parses overview.md to extract project metadata); search_arxiv() now deduplicates against known_ids with early termination after 5 consecutive known papers; full rename from weekly to daily report; research/CLAUDE.md updated.
  • ✅ error_recovery_benchmark: Multi-task error scenario visualization video generation (coffee/stack/three_piece_assembly) — coffee/stack (session 1): GPU6 generated 9 MP4s in parallel covering 6 error types including grasp_miss/grasp_wrong_pose/tip_over. three_piece_assembly (session 2): injection mode produced 0 valid scenes (base too physically stable), so AI proactively pivoted to visualizing the existing 158 natural error scenes, with 3 GPUs producing 4 MP4s (308–496KB) covering grasp_wrong_pose/premature_release/grasp_miss/overshoot.

Implementation & Fixes

  • ✅ VLA-RoboTwin: Fix collect_data.sh Vulkan rendering initialization failure — sapien.SapienRenderer() raised a RuntimeError on HPC headless nodes. Fixed by adding three Vulkan environment variables (VK_ICD_FILENAMES, __EGL_VENDOR_LIBRARY_FILENAMES, LD_LIBRARY_PATH) to collect_data.sh, referencing eval.sh in the same repository. Also located the episode_num: 50 config in task_config/demo_clean.yml.
  • 🔄 error_recovery_benchmark: Monitor multi-GPU parallel benchmark progress — GPU0 running coffee task (rollout 99/200), GPU6 running stack task (rollout 33/200). Existing scenes: pick_place 743, stack_three 264, stack 169, coffee/threading/three_piece_assembly 150+ each.
  • ✅ error_recovery_benchmark: Improve CLAUDE.md and README.md — Removed redundant Project Documentation, Directory Notes, and Related Project sections, moved test fixtures documentation to a more appropriate location, and fixed sections in README.md that still referenced old symlink paths.

Problems & Solutions

Critical Issues

1. Natural scenario visualization only showed 10 neutral frames, failing to capture actual policy behavior (pre-error trajectory data was not persisted during generation)

Solution: Record full action_history and initial_state in capture_natural_errors in rollout_generator.py and save to NPZ; replay from initial_state or 5 seconds before the error during visualization.

Key Insight: Pre-error trajectory data must be actively persisted during the scene generation phase. Demo replay (with HDF5) and policy replay (requiring extra storage) are two distinct sources that require separate storage design.

2. critical_region collected data all zeros: τ=0.10m threshold was purely estimated and does not match the actual robot workspace scale

Solution: Instead of directly tuning the parameter, record raw ee_target_distances (4–8 target distance variables per task), analyze the real data distribution, then calibrate the threshold.

Key Insight: Robot task space scales deviate significantly from intuition. Experiment-driven approaches (observe data first, then set threshold) are more reliable than parameter estimation. The raw distance recordings are themselves a valuable diagnostic tool.

3. three_piece_assembly injection mode generated 0 valid scenes: base is too physically stable; 3–35N impulse forces are insufficient to pass validation

Solution: Pivoted to visualizing the existing 158 natural error scenes covering 6 real failure types, with 3 GPUs producing 4 MP4s in parallel.

Key Insight: Injection parameters require task-specific calibration. Proactively pivoting to existing data when blocked is a reasonable adaptation — no need to wait for user instructions.

4. AI initially suggested waiting for injection tasks to finish before generating visualization, failing to proactively identify parallel execution opportunities

Solution: User pointed out that visualization could proceed immediately in parallel. AI then inserted lightweight visualization tasks into GPU low-load gaps.

Key Insight: Long-running GPU tasks and lightweight visualization tasks can run in parallel on the same GPU. Such opportunities should be proactively identified rather than defaulting to conservative serial waiting.

General Issues

5. GPU rendering environment misconfiguration: MUJOCO_EGL_DEVICE_ID and CUDA_VISIBLE_DEVICES physical IDs were mismatched, or HPC headless nodes were missing Vulkan driver configuration, causing rendering initialization failures

Solution: Set MUJOCO_EGL_DEVICE_ID and CUDA_VISIBLE_DEVICES to the same physical device ID. Reference eval.sh in the same repository and add VK_ICD_FILENAMES, __EGL_VENDOR_LIBRARY_FILENAMES, and LD_LIBRARY_PATH to collect_data.sh.

Key Insight: EGL/Vulkan device configuration is independent from CUDA. A working CUDA setup does not imply Vulkan/EGL availability. The solution usually already exists in other scripts in the same repository — reuse existing configuration first.

Solution: Changed .parent.parent to .parent.parent.parent, recalculating based on the real directory hierarchy (pipeline/ → scripts/ → error_benchmark/ → project_root).

Key Insight: After symlink removal, all path calculations that relied on symlink collapsing must be thoroughly reviewed. This is an easy-to-miss pitfall in large-scale migrations.

7. SSH remote script path errors: SSH defaults to the home directory and cannot find project script files; multiple incremental adjustments still failed

Solution: Use absolute paths for script paths in SSH commands, and place the cd operation and the main command in the same bash -c string.

Key Insight: SSH connections do not inherit the local working directory — absolute paths must be specified explicitly. Diagnosis should target root causes directly, not iterate through incremental adjustments.

Human Thinking vs. AI Thinking

Strategic Level

Data-Driven Diagnosis vs. Parameter Estimation

Role Approach
Human User actually opened the HDF5 file to verify labels were all zeros, inferred the threshold was too small, and proactively requested recording raw distances rather than directly asking to tune the parameter.
AI AI designed the τ=0.10m threshold based on pre_grasp_dis parameter estimation without suggesting collecting a small amount of data to validate first. Pivoted to recording raw distances after user feedback.

Analysis: The user’s experiment-driven thinking (validate first → diagnose → adjust) is more rigorous than AI’s direct estimation. AI tends to offer direct solutions without a validation step.

Role Approach
Human User prepared a complete 9-phase plan in advance, precisely listing all affected files, replacement patterns, and import path boundaries, explicitly distinguishing import statements from general terms in comments.
AI AI received the plan, split Phases 1–6 across 4 parallel sub-agents, handled Makefile/docs on the main thread, and proactively discovered and fixed 2 sys.path errors not listed in the plan.

Analysis: Human provided planning precision; AI provided parallel execution and proactively covered edge cases. The two complemented each other for efficient collaboration.

Identifying Parallel Execution Opportunities and Task Pivot Strategy

Role Approach
Human User directly identified that visualization could be generated immediately while injection tasks were running, without needing to wait serially. User expected injection output but accepted the AI’s proactively proposed alternative.
AI AI initially suggested serial waiting and did not proactively identify the parallel opportunity. However, after the three_piece_assembly injection failed, AI proactively explained the reason and proposed a pivot (visualizing natural error scenes) without waiting for user instruction.

Analysis: User was more proactive in identifying parallelism in task orchestration. AI was effective at pivoting when facing failures, but tended toward conservative serial planning initially.

Translating User Requirements into Engineering Solutions

Role Approach
Human User stated requirements from a product perspective (video should play from 5 seconds before the error; search and summarize a professor’s papers). Requirements were direct but without implementation specifics.
AI AI translated product requirements into engineering solutions (NPZ storage extension + replay logic; dual modes + mutually exclusive parameters + caching) and used AskUserQuestion to proactively clarify ambiguous requirements (whether author search needed LLM evaluation).

Analysis: User provided WHAT; AI designed HOW. The proactive clarification step converted ambiguous requirements into more complete engineering solutions.

AI Limitations

Significant Limitations

  • Lack of experimental validation in parameter setting: The τ=0.10m threshold was purely estimated without suggesting prior validation, resulting in an entire batch of critical_region data being all zeros. When building the srun command, EGL/CUDA device ID consistency was not checked in advance, causing the first run to fail.
  • Lack of task-specific physical intuition: Unable to anticipate the stability of the three_piece_assembly base under 3–35N impulse forces; could only discover through actual execution that injection produced 0 valid scenes.
  • Initial task orchestration tendency toward conservative serial execution: Failed to proactively identify GPU parallel execution opportunities (lightweight visualization could be inserted while injection was running); needed the user to point it out before switching.

General Limitations

  • Low efficiency in diagnosing repetitive errors: The SSH path issue required multiple incremental adjustments before identifying the root cause (absolute paths required). Diagnosis should target root causes directly.
  • Insufficient context management: The pre-error replay feature was interrupted by context exhaustion before the visualization replay logic was completed. Large feature development should plan session breakpoints in advance.

Today’s Takeaways

Core Takeaways

  • 4 parallel sub-agents + direct main-thread handling can complete a 65-symlink large-scale code migration within a single session. The key is dividing agent tasks into non-overlapping file sets, with the main thread handling Makefile/documentation work that cannot be parallelized.
  • Experiment-driven beats parameter estimation: Robot task space scales deviate significantly from intuition. Critical parameters (such as distance thresholds) should be set only after collecting a small amount of data to analyze the actual distribution. The raw recording of ee_target_distances is itself a valuable diagnostic tool.
  • Pre-error trajectory data (action_history + initial_state) must be saved to NPZ during the scene generation phase; otherwise visualization cannot reproduce actual policy behavior. Demo replay (with HDF5) and policy replay (requiring extra storage) must be designed with separate storage structures.
  • The physical properties of three_piece_assembly make the injection mode ineffective (base too stable); error scenes must be collected via natural_capture. The natural error distribution (grasp_wrong_pose 32%, premature_release 25%) reveals that the BC-RNN main bottleneck is in grasping, not insertion.
  • Using the template method pattern in an inheritance hierarchy (base class returns default value, subclass overrides as needed) is more consistent with the open-closed principle than using conditional branches in get_obs(). Adding a new task only requires implementing a subclass method without modifying the core flow.
  • arXiv supports au:“Author Name” exact author queries combinable with keywords (au:“Name” AND (kw1 OR kw2)), which is an effective way to systematically track a specific researcher’s latest work.

Practical Takeaways

  • GPU rendering environments are independently configured: EGL device ID must match the CUDA physical device ID. Vulkan drivers (VK_ICD_FILENAMES) are independent from CUDA — a working CUDA setup does not imply Vulkan availability. The solution usually already exists in other scripts in the same repository; reuse it first.
  • After symlink removal, sys.path parent chain depth must be recalculated based on the real directory hierarchy. Path.resolve() no longer collapses through symlinks, so parent chains that previously worked may now be too shallow — an easy-to-miss pitfall in large-scale migrations.

Session Summaries

Error Recovery Benchmark

🔄 GPU monitoring, multi-task error scenario visualization, full 65-symlink cleanup (planning → implementation), BC-RNN analysis and pre-error replay development 00:46:15.559 | claude_code 6 sessions across the day covering four main threads: ① GPU monitoring and coffee/stack visualization (session 1): checked GPU progress, user corrected AI’s serial-wait suggestion, generated 9 MP4s covering 6 error types in parallel on GPU6, resolved SSH working directory issue along the way. ② CLAUDE.md improvements (session 2): removed 3 redundant sections, fixed old path references in README.md. ③ Full 65-symlink cleanup (session 3 planning + session 2 implementation): session 3 planned a detailed 9-phase approach, session 2 completed full implementation via 4 parallel sub-agents (127 tests all green), AI also fixed 2 unplanned sys.path parent chain depth errors. ④ BC-RNN three_piece_assembly visualization and pre-error replay (session 2): injection mode produced 0 valid scenes, AI proactively pivoted to natural error visualization (4 MP4s); began implementing pre-error replay storage layer, interrupted by context exhaustion before the visualization replay logic was completed.

VLA-RoboTwin

✅ Critical region heuristic annotation system implementation (10 tasks), threshold calibration distance recording, Vulkan rendering fix 03:16:44.908 | claude_code 3 effective sessions: ① Critical region annotation design and implementation: read _base_task.py and 10 task implementations, designed three annotation strategies based on end-effector-to-target distance (τ=0.10m), implemented the get_critical_region_label() template method, all syntax checks passed. ② Distance recording extension: user discovered critical_region was all zeros and inferred threshold was too small, requested recording raw distances; added get_ee_target_distances() for all 10 tasks (4–8 distance variables per task), also fixed a dictionary self-parsing design flaw in place_dual_shoes. ③ Vulkan rendering fix and config location: added 3 Vulkan environment variables to collect_data.sh to fix headless rendering failure, located the episode_num: 50 config in task_config/demo_clean.yml.

gadget Research Scout

✅ from-overview initialization, search deduplication early-stop, full author search pipeline implementation 00:45:55.000 | claude_code 3 sessions: ① from-overview and deduplication early-stop: init –from-overview (LLM parses overview.md), full rename from weekly to daily report, search_arxiv() deduplication against known_ids with early termination after 5 consecutive known papers, CLAUDE.md updated. ② Requirements confirmation and planning: /init reviewed CLAUDE.md to document new features, used AskUserQuestion to confirm that author search required LLM evaluation mode, planned the –author parameter approach. ③ Author search implementation: added search_arxiv_author() (au: query + keyword combination + lookback_days filter + independent cache naming), added author branch and –conference mutual exclusion check to cmd_search() and cmd_report(), syntax check passed, –help confirmed working.

Token Usage

Overview

Metric Value
Total Tokens 64,063,345
Input Tokens 71,541
Output Tokens 201,075
Cache Creation 3,241,928
Cache Read 60,548,801
Cache Hit Rate 94.9%
Total Cost (USD) $46.1756

Model Breakdown

Model Input Output Cache Creation Cache Read Cost Share
claude-opus-4-6 32,543 133,280 2,289,190 51,874,237 $43.7393 94.7%
claude-haiku-4-5-20251001 38,998 67,795 952,738 8,674,564 $2.4364 5.3%