Daily Report — 2026-02-20
Daily Overview
- What was done: Implemented caching architecture for feature extraction benchmarks and debugged multi-environment policy evaluation pipelines to unlock milestone progress
- How it was done: Integrated CacheManager directly into existing benchmark scripts, corrected observation tensor mapping in rollout generators, and patched third-party API compatibility issues
- Impact: Eliminated redundant model inference loops, restored end-to-end evaluation for critical milestones, and successfully orchestrated high-throughput GPU workloads
DCC
- What was done: Added cache read/write support to the MIHD benchmark framework and identified preprocessing drift in fusion evaluation metrics
- How it was done: Modified argument parsing, injected variant-aware cache checks before encoder instantiation, and documented dual-normalization bugs in STAIG pipelines
- Impact: Enabled instant loading of pre-computed embeddings, drastically reducing benchmark turnaround time for image-only clustering comparisons
tianhe
- What was done: Debugged observation dimension mismatches in BC-RNN policy evaluation and prepared infrastructure for M12-M14 milestone validation
- How it was done: Corrected state-to-observation key mapping, updated RoboMimic utils for v0.3.0 compatibility, and orchestrated parallel GPU background processes
- Impact: Resolved critical blocking bugs that prevented baseline evaluations, successfully launching 6 concurrent GPU jobs for scene generation and VLA server setups
Accelerated MIHD benchmark infrastructure with embedding cache support and resolved critical observation pipeline mismatches in the Error Recovery Benchmark, enabling large-scale parallel GPU evaluations across both projects.
Tasks
Architecture & Strategy
- ✅ Implement embedding cache I/O in run_benchmark.py — Integrated CacheManager to bypass redundant encoder instantiation, adding variant tracking for standard/freq/staig_strict modes and relaxing argparse constraints for custom encoder names.
- ✅ Fix BC-RNN observation dimension mismatch & rollout flow — Corrected _to_robosuite_obs() key mapping and patched both injection and capture methods to forward raw robosuite observations instead of flattened state_info, resolving tensor shape errors.
- 🔄 Orchestrate M14 baseline evaluation & parallel GPU workloads — Launched concurrent background jobs for BC-RNN injection/natural capture, demo generation, and Pi0/Pi0.5 VLA servers while continuing background job monitoring and log analysis.
Implementation & Fixes
- ✅ Patch RoboMimic v0.3.0 API compatibility in MimicGen utilities — Inspected create_env_for_data_processing signature and updated the wrapper to only pass supported arguments, preventing runtime EOF crashes during dataset generation.
Problems & Solutions
Critical Issues
1. run_benchmark.py lacked cache loading, causing redundant encoding iterations; AI initially proposed writing a standalone evaluation script to fix comparison inconsistencies.
Solution: User corrected the architectural direction: integrate CacheManager directly into the existing benchmark script with variant tracking instead of creating parallel tools that risk preprocessing drift.
Key Insight: Leveraging established pipeline infrastructure is more robust and maintainable than building isolated evaluation wrappers, especially when feature normalization must be strictly preserved across strategies.
2. eval_scan_fusion.py produced artificially lower STAIG fusion ARI scores due to double normalization (external preprocessing + internal StandardScaler) caused by missing alignment config.
Solution: Identified the root cause and documented that passing correct staig_alignment_config to apply_fusion() triggers align_staig=True, automatically skipping redundant scaling in the trainer.
Key Insight: When evaluating models with built-in feature alignment or normalization layers, external preprocessing pipelines must either pass explicit override flags or skip normalization entirely to avoid signal distortion.
3. BC-RNN policy evaluation crashed during rollout due to input size mismatch (checkpoint expects 65-dim, environment provides 37-dim), and state_info incorrectly replaced raw observations.
Solution: Fixed key mapping in _to_robosuite_obs() to align training modalities and updated both _generate_from_single_rollout and _capture_single_rollout to pass raw robosuite dicts during initialization.
Key Insight: Policy inference pipelines must strictly maintain original observation modalities during rollout, as internal state extractors often drop or compress keys required by pre-trained adapters.
General Issues
4. MimicGen dataset generation failed due to outdated kwargs passed to create_env_for_data_processing in RoboMimic 0.3.0, causing environment instantiation crashes.
Solution: Dynamically inspected the live API signature and updated the wrapper to conditionally pass only supported arguments, falling back gracefully when environment metadata lacks expected fields.
Key Insight: Version pinning for third-party ML environments is critical; dynamic signature inspection or strict version constraints prevent silent API breakage during data pipeline execution.
Human vs AI Approaches
Strategic Level
Benchmark architecture strategy for MIHD embedding caching
| Role | Approach |
|---|---|
| Human | User explicitly instructed to modify run_benchmark.py directly to use pipeline cache instead of creating independent scripts, ensuring unified preprocessing compliance and avoiding feature drift. |
| AI | AI initially proposed writing temporary evaluation scripts (_test_staig_scan.py) and later suggested architectural refactors that duplicated existing pipeline logic, missing the opportunity for system cohesion. |
Difference Analysis: Human prioritized infrastructure consistency and long-term maintainability by leveraging established pipelines, while AI optimized for immediate isolation, which introduced unnecessary complexity and normalization risks.
BC-RNN observation pipeline debugging in Error Recovery Benchmark
| Role | Approach |
|---|---|
| Human | User identified the exact dimension mismatch context and flagged that state_info was incorrectly replacing raw robosuite observations during policy prediction initialization. |
| AI | AI traced the rollout flow, corrected tensor key mapping in adapters, and patched both evaluation methods, but initially misinterpreted why the crash occurred without explicit shape tracing guidance. |
Difference Analysis: Human provided precise diagnostic context regarding training vs inference tensor shapes, enabling direct code patching without unnecessary refactoring, whereas AI relied on broader structural debugging first.
AI Limitations
General Limitations
- AI initially misinterpreted the caching mechanism of UNI2VisionEncoder and struggled to track multi-context state across interrupted sessions, leading to redundant tool calls before corrections were applied.
- During parallel job orchestration, AI lacked automated port conflict detection for VLA servers, requiring manual cleanup and restart sequences to resolve bind failures.
Learnings
Key Learnings
- When modifying benchmark infrastructure, always verify if the existing pipeline already handles caching or normalization; forcing parallel implementations often introduces subtle preprocessing drift bugs that skew comparative metrics.
- Policy evaluation pipelines must strictly maintain original observation modalities during rollout initialization, as internal state extractors frequently drop or compress keys required by pre-trained models, leading to silent tensor mismatches.
Conversation Summaries
MIHD Benchmark Cache Integration
✅ Implementing embedding cache and fixing STAIG fusion evaluation 18:06:33 | claude_code The session focused on enhancing the MIHD benchmark pipeline by integrating CacheManager directly into run_benchmark.py to skip redundant encoder instantiation. The user redirected the AI from creating standalone scripts, ensuring preprocessing alignment. A critical bug was identified where eval_scan_fusion.py applied double normalization to STAIG gene features, artificially lowering ARI scores. The plan was updated to use the official benchmark script for accurate SCAN vs UNI2 comparisons.
Error Recovery M14 Infrastructure & Debugging
✅ Debugging BC-RNN obs flow and launching parallel GPU evaluations 19:00:40 | claude_code Progress was checked against the M12-M14 milestone checklist. The AI implemented save_images logging in the data collector, verified MimicGen configs, and corrected taxonomy documents. A major blocking issue arose when BC-RNN policy evaluation failed due to an observation dimension mismatch. The AI fixed state-to-observation mapping, patched Robomimic v0.3.0 API compatibility, and successfully launched parallel GPU workloads for scene generation and VLA server setups.
Error Recovery Baseline Orchestration
• Large-scale GPU job management and environment fixes 02:39:21 | claude_code Following initial diagnostic runs, the AI continued debugging background jobs that failed due to API mismatches and state flow issues. Corrections were applied to policy adapters and robustness utilities. The session concluded with orchestrating multiple GPU processes across CUDA devices for BC-RNN injection/natural capture, demo generation, and Pi0/Pi0.5 VLA servers, shifting focus from broken MimicGen paths to stable policy rollouts.