Daily Report — 2026-03-20

Today’s Overview

  • What was done: Simultaneously advanced two research directions across DCC and tianhe HPC clusters — robotic manipulation and spatial transcriptomics. The former covered data quality fixes for the error recovery benchmark, parallel batch generation of training scenes, and building the Pi0.5-base LoRA fine-tuning pipeline; the latter completed a fair-comparison fix for STAIG hyperparameter tuning and developed 10x Visium data analysis scripts.
  • How it was done: Localized and fixed the false-positive threshold issue via systematic GPU diagnostic scripts; refactored serial scene generation into a 32-worker ThreadPoolExecutor with 4-GPU round-robin scheduling; completed HDF5→LeRoBot format conversion and norm stats computation; fixed experiment naming confusion through cross-referencing logs and cache; migrated Visium visualization to scanpy’s native API.
  • Outcomes: drop_in_transit false-positive rate dropped from 80% to 0%; scene generation speed improved ~10× (973 scenes in 41 minutes); all upstream data for Pi0.5 training is ready; STAIG fair baselines established; all 6 Visium visualizations generated correctly. Training was interrupted when the Slurm interactive job expired and must be resubmitted via sbatch.

DCC

  • What was done: Fixed the unfair comparison in the STAIG hyperparameter sweep for the MIHD project, and completed scanpy analysis script development and visualization fixes for the 10x Visium 151676 sample in the claude-demo project.
  • How it was done: Discovered through log and cache inspection that pca_uni2_staig_fusion was actually using UNI rather than UNI2; modified the ablation script to support gene/vision encoder parameter configuration; changed Visium visualization from plt.savefig to scanpy’s native save parameter.
  • Outcomes: Established a fair baseline comparison of PCA+UNI vs PCA+UNI2 (mean ARI 0.47); all 6 spatial transcriptomics plots (QC, PCA, UMAP, spatial expression) generated correctly.

tianhe

  • What was done: Diagnosed and fixed the drop_in_transit_D0 false-positive issue; refactored 6-task training scene generation from serial to 32-worker parallel (973 scenes in 41 minutes); completed data preparation for the Pi0.5-base LoRA fine-tuning pipeline; briefly launched coffee/stack training (interrupted by Slurm job expiry).
  • How it was done: Wrote a GPU diagnostic script to pinpoint the min_hold_height threshold issue; created generate_training_scenes_parallel.py (ThreadPoolExecutor with 4-GPU round-robin); executed HDF5→LeRoBot conversion, norm stats computation, and training launch on an49/an50 nodes via SSH+tmux; cross-analyzed parallel_logs and opportunity maps to document failure root causes.
  • Outcomes: drop_in_transit false positives eliminated (D0 success rate 10%→50%); 973 training scenes ready; Pi0.5 6-task data pipeline prepared; root-cause documentation provides clear direction for subsequent validator fixes; training interrupted by Slurm limits, sbatch job resubmission required.

Simultaneously advanced four workstreams across DCC and tianhe HPC clusters: fixed drop_in_transit false positives in the error recovery benchmark and generated 973 training scenes for 6 tasks in parallel with 32 workers (41 minutes); built a Pi0.5-base LoRA fine-tuning pipeline for a merged 6-task dataset (data conversion and norm stats complete, training interrupted by Slurm job expiry); fixed unfair comparison in STAIG hyperparameter sweep and established PCA+UNI/UNI2 baselines (ARI 0.47); and developed a scanpy analysis script for 10x Visium spatial transcriptomics.

Today’s Tasks

Architecture & Strategy

  • ✅ drop_in_transit_D0 false-positive diagnosis and fix — GPU diagnostic scripts confirmed that min_hold_height=0.85 caused 8/10 opportunities to be false positives (object on table surface at z≈0.88>0.85). Raised the threshold to 0.93 (table height 0.80 + object height 0.08 + margin 0.05) across 5 error skill files and the config. False-positive rate dropped from 80% to 0%; D0 success rate improved from 10% to 50% (4/8).
  • ✅ Parallel batch generation of 6-task training error scenes — Refactored the serial script into a parallel version with –subtypes filter support; created generate_training_scenes_parallel.py (32 workers, 4-GPU round-robin); ran via nohup in background on an50 node, generating 973 scenes in 41 minutes (128/130 workers successful), ~10× faster than serial.
  • • Pi0.5-base LoRA merged-dataset fine-tuning pipeline setup — Redirected openpi05 conda environment .pth file; added 12 merged configs to config.py (6 tasks × finetune/inference); completed HDF5→LeRoBot data conversion for 5 tasks (coffee/stack/stack_three/three_piece_assembly 2000 episodes, threading 1000 episodes) and norm stats computation. Launched coffee/stack training in an49 tmux; all processes killed when the Slurm interactive job expired — sbatch resubmission required.
  • ✅ Training scene generation failure root-cause analysis and documentation — Cross-analyzed parallel_logs, opportunity map distributions, and meta.json to identify 5 root causes (gripper_closed_norm anomaly P0-level, insufficient drop collision detection, OSC displacement response, object physical constraints, threading phase mismatch); documented in training_scene_failure_analysis.md as actionable reference for subsequent validator fixes.
  • ✅ STAIG hyperparameter sweep fair-comparison fix — Discovered that the ablation script was using raw HVG 3000-dim input rather than PCA, and that experiment names were misleading (actually using UNI rather than UNI2). Modified the script to support –gene_encoder and –vision_variant parameters; re-ran pseudo_k sweep with PCA+UNI and PCA+UNI2 respectively; mean ARI ~0.47 for both, establishing fair baselines.
  • 🔄 Adding pick_place as the 6th task to the training pipeline — Modified train_pi05_merged.py, config.py, and MimicGen config (2000 D0 episodes, no D1 variant). Data generation started on an49 GPU 0 but was interrupted by Slurm job expiry (completed 184/2000).

Implementation & Fixes

  • ✅ 10x Visium data analysis script development and visualization fix — Wrote a scanpy analysis script for the 151676 sample (QC, normalization, HVG, PCA/UMAP, spatial visualization); fixed spatial_gene_expression.png showing only H&E background with no gene expression spots: switched from plt.savefig to scanpy’s native save parameter (sc.settings.figdir + save), allowing scanpy’s internal pipeline to handle spot rendering. All 6 plots generated correctly.
  • ✅ openpi Docker image build and save guide — Read openpi’s docs/docker.md, serve_policy.Dockerfile, and compose.yml; summarized the purpose and build commands for all 5 Dockerfiles, as well as the complete docker save/load workflow.

Problems & Solutions

Key Issues

1. drop_in_transit_D0 generating very few valid scenes — diagnosis confirmed min_hold_height=0.85 threshold too low, causing objects on the table surface (z≈0.88) to be misclassified as held mid-air, with 80% of opportunities being false positives

Solution: Raised min_hold_height from 0.85 to 0.93 (table height 0.80 + Milk object height 0.08 + margin 0.05); updated 5 error skill files and config synchronously. False positives eliminated; D0 success rate improved from 10% to 50%.

Key insight: In pick_place tasks, the z-height of objects resting on the table can exceed a naively set min_height. Designing grasp-detection thresholds must account for the combined stack of “table height + object height” plus a safety margin.

2. Training scene generation script fully serial; collision_eef_object subtype success rate extremely low (5 scenes in 10 minutes); 6 tasks estimated to take 6+ hours

Solution: Added –subtypes filter parameter to the script; created a ThreadPoolExecutor-based parallel launcher (32 workers, 4-GPU round-robin); completed all 973 scenes in 41 minutes.

Key insight: MuJoCo EGL rendering is CPU-intensive; 32+ independent processes can run concurrently on the same node without conflict. Subprocess-based parallelism is more stable than multithreading and fully utilizes a 48-core CPU.

3. After the Slurm interactive job expired, all training and data generation processes launched via SSH were killed, wasting training time already invested

Solution: Must submit formal jobs via sbatch, or use tmux within an allocated job to ensure process persistence after terminal disconnect. The pam_slurm_adopt policy kills all associated processes when the job ends — SSH nohup cannot bypass this.

Key insight: Slurm’s resource isolation on HPC clusters forcibly cleans up all processes within a job, including subprocesses of SSH connections. Long-running training jobs must use sbatch; interactive jobs cannot be relied upon.

4. 29 task×subtype combinations completely missing after 128/130 workers finished; threading only generated 3/29 subtypes

Solution: Cross-analyzed opportunity maps and generation logs to distinguish two failure classes: no opportunities found during the opportunity scan phase vs. validator rejection during generation. Prioritized fixing gripper_closed_norm=0.00 anomaly (P0-level; would unlock 8 combinations).

Key insight: Scene generation failures fall into two stages: opportunity scan (can_inject always False) and validator rejection. Fix strategies differ: modify can_inject conditions for the former, adjust thresholds or injection parameters for the latter.

5. Abnormally large gap between STAIG hyperparameter sweep baseline ARI (0.23) and clustering ablation ARI (0.58); experiment name ‘pca_uni2_staig_fusion’ is misleading — actually uses UNI rather than UNI2; ablation script uses raw HVG rather than PCA input

Solution: Modified the ablation script to add –gene_encoder and –vision_variant parameters; re-ran sweep with PCA+UNI/UNI2; mean ARI ~0.47 (reasonable range), establishing fair baselines.

Key insight: Experiment naming conventions must strictly match the implementation. Misleading names cause long-term misunderstandings. Always confirm actual implementation by reading logs rather than relying on experiment names alone.

6. PickPlace task has no MimicGen D1 variant, making the originally planned D0+D1 merged dataset infeasible

Solution: Generated 2000 D0 episodes directly as an equivalent replacement for the D0+D1 merged approach.

Key insight: The number of variants available in MimicGen varies by robosuite task (e.g., PickPlace only has D0). Multi-task data strategies must confirm available variants per task; total-count equivalence (e.g., 2000 D0 in place of 1000 D0 + 1000 D1) is a valid engineering decision.

General Issues

7. Visium sc.pl.spatial generating images that show only the tissue H&E background with no gene expression spots overlaid

Solution: Changed all sc.pl.* save calls from plt.savefig to scanpy’s native save parameter (sc.settings.figdir + save), letting scanpy’s internal pipeline handle spot rendering.

Key insight: Scanpy’s spatial plots must be saved through its internal save mechanism. Bypassing it with plt.savefig directly skips the spot rendering step, resulting in incomplete images.

8. MUJOCO_EGL_DEVICE_ID=0 and CUDA_VISIBLE_DEVICES=1 mismatch causing import failure

Solution: Set MUJOCO_EGL_DEVICE_ID to match CUDA_VISIBLE_DEVICES (both set to 1).

Key insight: MUJOCO_EGL_DEVICE_ID must be the global physical GPU ID, not the remapped index produced by CUDA_VISIBLE_DEVICES.

Human Thinking vs AI Thinking

Strategic Level

Proactiveness in system resource utilization and efficiency optimization

Role Approach
Human User proactively noticed that the serial process was running slowly (5 scenes in 10 minutes) and proposed “we can easily do 32+ parallel now.” Also noticed the anomalous single-episode training data count and pushed for deeper investigation. Drove efficiency optimization decisions from a global system resource perspective (48-core CPU, 4 GPUs, ample memory).
AI Executed the serial plan as written; upon noticing slowness, set a 30-minute check timer and waited. Required user prompting to begin diagnosis on the data anomaly. Lacked proactive assessment of overall throughput and system resource utilization.

Analysis: Humans proactively proposed optimization from a system resource and overall efficiency perspective; the AI focused on completing the current task and lacked the awareness to ask “can we do better?”

HPC process persistence strategy (understanding Slurm mechanics)

Role Approach
Human Explicitly noted that nohup via SSH is unreliable; required tmux or Slurm to ensure process persistence after terminal disconnect; demonstrated real-world HPC experience and familiarity with the pam_slurm_adopt mechanism.
AI Initially launched processes via SSH with nohup + &, without considering process isolation from Slurm job expiry.

Analysis: Humans understood the underlying HPC resource management mechanism; the AI only considered shell-level process backgrounding and overlooked Slurm resource isolation.

Intuitive judgment on experimental fairness and data strategy

Role Approach
Human Proactively noticed the unreasonable ARI gap (0.23 vs 0.58), directly questioned experimental fairness, and proposed a concrete fix; quickly decided to use 2000 D0 episodes as an equivalent substitute when PickPlace had no D1 variant, without needing to enumerate all technical possibilities first.
AI Did not proactively identify the experimental unfairness; began forensic log analysis only after being prompted. Tended toward exploring more options before making data strategy decisions — relatively conservative.

Analysis: Humans made practical decisions quickly from a global experimental design perspective; the AI handled tasks at the execution level but lacked proactive review and rapid decision-making.

Depth of failure analysis driven by actionability

Role Approach
Human Required that every error’s detector/injector/validator details be documented, with the explicit goal of providing actionable reference for future fixes — framing the analysis from the perspective of the person who will do the repairs.
AI Provided a statistical list of failing subtypes and root-cause categories, but did not proactively integrate code-level implementation details into an actionable repair reference document.

Analysis: Humans drove documentation content by asking “how do we fix this later?”; the AI stayed at the level of describing phenomena.

AI Limitations

Key Limitations

  • Repeatedly failed to proactively identify efficiency bottlenecks and propose optimizations: when serial scene generation was slow, only set a timer and waited (did not suggest parallelization); when launching training processes via SSH nohup, did not consider Slurm job expiry resource isolation. Both required user intervention to correct, resulting in actual time wasted.
  • Failed to proactively detect inconsistencies between experiment configuration and naming (‘pca_uni2_staig_fusion’ actually using UNI), and the discrepancy between meta.json statistics (current run) and on-disk file counts (historical accumulation). Both required user questioning before log-based forensics confirmed the issue.

General Limitations

  • Multiple tool/API usage detail errors: scanpy spatial plot using plt.savefig bypassed internal rendering, causing missing plot elements; tyro boolean flag using –resume=True format caused training script crash; MUJOCO_EGL_DEVICE_ID and CUDA_VISIBLE_DEVICES mismatch caused import failure.
  • Lacked fast fallback strategies when resources/tools were unavailable (unstable HuggingFace proxy, TaskOutput tool call failures, progress polling); only offered alternative suggestions after multiple failures, consuming significant time.

Today’s Takeaways

Core Takeaways

  • Slurm HPC’s pam_slurm_adopt policy forcibly kills all associated processes when a job ends; SSH nohup cannot bypass this. Long-running training jobs must be submitted via sbatch — interactive jobs cannot be relied upon.
  • Height threshold design for robot manipulation scene generation must account for the combined interval of “table height + object height.” A naively set min_height that ignores the stacked height of specific objects will produce massive false positives and very few valid scenes.
  • MuJoCo EGL simulation is CPU-intensive; 32+ independent processes can run concurrently on the same node without conflict. Training scene generation failures fall into two stages (opportunity scan with can_inject always False vs. validator rejection) and must be handled differently: modify can_inject conditions for the former, adjust thresholds or injection parameters for the latter.
  • Experiment naming conventions must strictly correspond to actual implementation. Naming inconsistencies (e.g., ‘pca_uni2’ actually using UNI) lead to long-term experimental misunderstandings. Actual configuration must be confirmed by reading logs, not just by experiment names.
  • The number of D0/D1 variants in MimicGen varies by task (e.g., PickPlace only has D0). Multi-task data strategies require per-task confirmation of available variants; total-count equivalence (e.g., 2000 D0 in place of 1000 D0 + 1000 D1) is a valid engineering decision.
  • When diagnosing data generation issues, distinguish between “meta.json statistics for the current run” and “on-disk historically accumulated file counts” — a large discrepancy indicates the problem lies in current run logic, not historical accumulation. For parallel tasks, writing a separate log file per worker is recommended for fast grep-based failure localization.

Practical Takeaways

  • Scanpy’s spatial transcriptomics visualization functions such as sc.pl.spatial must be saved via sc.settings.figdir + save parameter. Using plt.savefig directly bypasses the internal layer rendering pipeline and produces incomplete images.

Session Summaries

Error Recovery Benchmark (Scene Fix & Parallel Generation)

✅ drop_in_transit_D0 false-positive fix + serial-to-parallel 6-task training scene generation + failure root-cause analysis 00:55:55.225 | claude_code Diagnosis confirmed that min_hold_height=0.85 caused widespread false positives (objects on table surface misclassified as held mid-air). Raising the threshold to 0.93 improved D0 success rate from 10% to 50%. Planned and executed batch generation of 6-task training scenes: initial serial execution revealed extreme slowness (5 scenes in 10 minutes); after user suggested parallelization, AI refactored to a 32-worker ThreadPoolExecutor (4-GPU round-robin), generating 973 scenes in 41 minutes (128/130 workers successful). Cross-analyzed parallel_logs and opportunity maps to identify 5 root causes (gripper_closed_norm anomaly at P0 severity, insufficient drop collision detection, etc.); output training_scene_failure_analysis.md.

Error Recovery Benchmark (Pi0.5 Training Pipeline)

🔄 Pi0.5-base LoRA merged-dataset fine-tuning pipeline: environment setup, data conversion, and training launch 01:08:53.326 | claude_code After thoroughly investigating the openpi repo structure and existing datasets, executed the plan: redirected openpi05 conda environment .pth file; added 12 merged configs to config.py (6 tasks × finetune/inference); completed HDF5→LeRoBot conversion and norm stats computation for 5 tasks. Launched coffee/stack training in an49 tmux; pick_place data generation progressed to 184/2000 before all processes were killed by Slurm interactive job expiry. threading_d1 temporarily replaced with d0-only due to unavailable HuggingFace proxy (TODO comment added).

MIHD Spatial Transcriptomics

✅ STAIG hyperparameter sweep fair-comparison fix: discovered naming discrepancy and inconsistent gene input 14:40:59.838 | claude_code User noticed an abnormally large gap between hyperparameter sweep baseline ARI (0.23) and clustering ablation ARI (0.58). AI confirmed through log inspection that pca_uni2_staig_fusion was actually using UNI rather than UNI2, and that the ablation script was using raw HVG instead of PCA input. After modifying the script to support –gene_encoder and –vision_variant parameters, re-ran pseudo_k sweep with PCA+UNI and PCA+UNI2; mean ARI ~0.47 for both, establishing fair baselines.

claude-demo (Visium Analysis)

✅ 10x Visium 151676 sample scanpy analysis script development and visualization fix 14:55:05.867 | claude_code Completed project initialization (CLAUDE.md creation + fixing broken symlinks); developed a complete scanpy analysis script for the 151676 Visium sample (data loading, QC, normalization, HVG, PCA/UMAP, spatial visualization). After discovering that spatial_gene_expression.png showed only H&E background with no gene spots, changed all sc.pl.* save calls from plt.savefig to scanpy’s native save parameter; all 6 plots generated correctly.

openpi VLA

✅ openpi project Docker image build and save method reference 04:50:34.000 | claude_code Read openpi’s docs/docker.md, serve_policy.Dockerfile, and compose.yml; summarized the purpose and build commands for all 5 Dockerfiles, as well as the complete docker save/docker load export and load workflow.

Token Usage

Overview

Metric Value
Total Tokens 91,266,639
Input Tokens 55,409
Output Tokens 195,151
Cache Write 4,302,013
Cache Read 86,714,066
Cache Hit Rate 95.3%
Total Cost (USD) $61.6229

Model Breakdown

Model Input Output Cache Write Cache Read Cost Share
claude-opus-4-6 29,725 121,342 3,083,310 71,441,071 $58.1775 94.4%
claude-haiku-4-5-20251001 25,684 73,809 1,218,703 15,272,995 $3.4454 5.6%

Usage by Device

Device Total Tokens Input Output Cost
DCC 5,601,785 1,456 12,539 $4.3744
tianhe 84,460,618 53,908 180,837 $56.3994
TzJsDesktop 1,204,236 45 1,775 $0.8491