Daily Log — 2026-02-14

Today’s Overview

• What I did: Implemented force-override parameters, expanded force range, and impulse-clearing logic in the robotic arm visualization script; successfully generated a test video with 30N injected force
• How I did it: Modified three files (visualization script, benchmark config, impulse injector) and ran everything directly on the current GPU node (A800×5), avoiding unnecessary SSH hops
• Why it matters: Established a complete toolchain for visualizing force debugging — but discovered that 30N is still insufficient to produce visible displacement on the Sawyer arm under OSC control, revealing a potentially deeper problem with the force injection mechanism

Implemented force injection enhancements for a robotic arm error-recovery benchmark, but 30N still produces no visible perturbation in video — root cause unresolved

Today’s Tasks

Architecture & Strategy

• ❌ Debug the root cause of 30N force injection still being ineffective — User suggested testing with infinite force to verify whether the injection mechanism itself works; session was interrupted before implementation
• ✅ Implement –force_override parameter and force injection enhancements — Added --force_override CLI argument to 2_visualize_scene.py to override force magnitude before Phase 2 injection; implemented automatic xfrc_applied clearing after duration_steps in the Phase 3 loop; updated configs/benchmark_v4.yaml to raise force_norm_range from [3.0, 15.0] to [15.0, 45.0] and force_clip from 30.0 to 60.0; fixed bug in ImpulseInjector reading nested config paths
• ✅ Generate and verify visualization video of 30N force injection — Ran 2_visualize_scene.py --force_override 30 on the current A800 node; generated impulse_demo_0_step72.mp4 (451KB, 193 frames), but visual inspection showed no visible arm perturbation

Implementation & Fixes

• ✅ Update CLAUDE.md with GPU node usage conventions — Documented that SSH-ing to a GPU node requires cd-ing to the project directory and activating the conda environment; added best practice of checking the current node for GPUs via nvidia-smi before attempting SSH

Problems & Solutions

Critical Issues

1. Applying 30N to the Sawyer arm produces zero visible displacement in video

Solution: Unresolved. User suggested testing with infinite force to diagnose whether the force injection mechanism is fundamentally broken.

Key insight: Possible causes: (1) duration_steps=1 means the impulse is cleared after ~20ms — fast enough for the OSC controller to compensate; (2) xfrc_applied may be applied to the wrong body or position; (3) the force direction may be nearly orthogonal to the OSC controller’s principal axes, causing rapid attenuation

2. Repeated SSH attempts to GPU nodes were rejected by pam_slurm_adopt (requires an active SLURM job), wasting significant time

Solution: User pointed out to check whether the current node already has GPUs (nvidia-smi -L); discovered the current node already had 5× A800, eliminating the need for SSH.

Key insight: On an HPC cluster accessed via a jump host, you may already be on a GPU node. Always check local GPU availability before attempting to hop elsewhere.

General Issues

3. AI repeatedly omitted cd in SSH commands, causing the same error (file path not found) to occur 15+ times

Solution: After repeated corrections from the user, the AI eventually understood that SSH-ing to a remote node starts a fresh session requiring an explicit cd to the project directory and conda activation.

Key insight: SSH does not inherit the current shell’s working directory — this is a systemic blind spot that needs to be explicitly documented in CLAUDE.md

4. AI unnecessarily added proxy setup (source setproxy.sh) to visualization commands

Solution: User noted that the visualization script only operates on local files (NPZ, HDF5, MuJoCo rendering) and requires no network access, making proxy setup meaningless.

Key insight: Proxy configuration is only relevant for pip install/conda/external network operations — local compute tasks need none of it

Human Reasoning vs. AI Reasoning

Strategic Level

Debugging approach after 30N force proved ineffective

Role	Approach
Human	Proposed testing with infinite force as an extreme sanity check to quickly determine whether the injection mechanism works at all (binary elimination)
AI	Tended toward detailed mechanical analysis and code review (planned to re-tune duration_steps and similar parameters)

Analysis: The human chose the fastest experiment to validate the core assumption; the AI favored systematic analysis but at greater time cost. For debugging, the human’s approach is more efficient.

How to obtain GPU access

Role	Approach
Human	Suggested checking the current node for GPUs via nvidia-smi first, avoiding unnecessary SSH hops
AI	Fell into a fixed mental model of “must SSH to a GPU node,” repeatedly trying different SLURM partitions and getting rejected each time

Analysis: The human reasoned from current environment state to find the shortest path; the AI fixated on a preset workflow and skipped the most basic step of checking the current situation.

Whether proxy setup was necessary

Role	Approach
Human	Clearly pointed out that visualization is a purely local operation, questioned why the AI added a proxy sourcing step
AI	Blindly reused a proxy command template seen in CLAUDE.md without considering whether the current task actually requires network access

Analysis: The human reasoned from task requirements to tool usage; the AI mechanically applied a historical template — reflecting a lack of understanding of why a command is used.

AI Limitations

Critical Limitations

• Made the same error (missing cd after SSH) 15+ times, failing in exactly the same way each time — indicating the AI lacks the ability to learn from repeated failures and correct its behavior within a single session
• After all available partitions were exhausted, the AI never independently considered “the current node might already have a GPU” as the simplest possibility — it required a human prompt

General Limitations

• When applying templates, the AI does not verify their applicability (proxy setup is for network operations, but was mechanically applied to a local compute task) — reflecting a lack of semantic understanding of tool purpose

Today’s Takeaways

Core Takeaways

• The OSC controller offers extremely strong resistance to external forces (kp=150, Λ≈5–10 kg); a duration_steps=1 (~20ms) impulse is cleared before the controller has time to react, making it effectively equivalent to no force at all. For perturbations to be visible, force must be applied continuously for at least several control cycles, or must greatly exceed the controller’s compensation capacity
• When using Claude Code on an HPC cluster, always run nvidia-smi -L to detect the current node’s GPUs before starting any GPU task. If GPUs are already available locally, SSH is unnecessary; if SSH is required, the remote command must explicitly cd to the project directory and activate the conda environment
• When debugging a force injection mechanism, start with extreme parameters (e.g., infinite force or 1000-step duration) to verify the mechanism works at all, then tune down to a reasonable range — rather than starting reasonable and incrementally increasing

Session Summary

🔄 Implement force injection enhancements to make arm perturbations visible in video 01:35:54.517 | claude_code Modified three files per a pre-made plan: added --force_override to the visualization script, updated benchmark config to raise force range from 3–15N to 15–45N, and fixed the nested config read bug in ImpulseInjector. GPU access then wasted significant time (AI repeatedly omitted cd in SSH commands; all SLURM partitions were rejected); the user eventually suggested checking the current node directly, revealing 5× A800 were already available. The video was generated and run directly. Post-verification confirmed that 30N remains insufficient to produce visible displacement on the Sawyer arm under OSC control. Session was interrupted while preparing to test with infinite force.

Token Usage

Overview

Metric	Value
Total Tokens	17,440,851
Input Tokens	18,919
Output Tokens	2,612
Cache Creation	1,924,142
Cache Read	15,495,178
Cache Hit Rate	89.0%
Total Cost (USD)	$7.2429

Model Breakdown

Model	Input	Output	Cache Creation	Cache Read	Cost	Share
claude-opus-4-6	865	340	329,265	2,331,807	$3.2366	44.7%
claude-haiku-4-5-20251001	9,712	1,788	1,412,273	12,219,193	$3.0059	41.5%
claude-sonnet-4-5-20250929	8,342	484	182,604	944,178	$1.0003	13.8%

Usage by Device

Device	Total Tokens	Input	Output	Cost
DCC	2,291,835	852	165	$1.4197
tianhe	12,727,538	13,561	2,252	$4.0629
TzJsDesktop	2,421,478	4,506	195	$1.7603