Daily Report — 2026-07-11
Daily Overview
- What was done: Audited model evaluation outputs across multiple datasets to identify failure cases and enhanced visualization tools for the Action-Sketcher project.
- How it was done: Utilized shell commands (find, grep) to parse large file systems and JSON manifests; modified Python source code to inject reasoning frames and trajectory overlays into MP4 videos.
- Impact: Provided critical data for model error analysis and improved the interpretability of robotic planning outputs.
athena
- What was done: Identified failure trajectories for Pi0.5 and GR00T models within the RoboCasa evaluation runs.
- How it was done: Sear directory structures for specific naming conventions (_failure.mp4 vs _s0.mp4) and parsed stats.json files for success rates.
- Impact: Mapped out the exact locations of 2,017 Pi0.5 failures and 1,210 GR00T failures for downstream analysis.
athena.egr.duke.edu
- What was done: Replicated the RoboCasa failure video identification process on the Athena cluster.
- How it was done: Executed identical bash scripts to locate and count failure/success videos in the runs/ directory.
- Impact: Validated the consistency of evaluation outputs across different environment states.
lighthouse
- What was done: Located specific failure cases for the Action-Sketcher model deployed on the Tianhe3 cluster.
- How it was done: SSH’d into the remote server, identified FAIL-labeled MP4s, and used SCP to transfer them locally.
- Impact: Secured concrete examples of model failures for qualitative review.
lighthouse.egr.duke.edu
- What was done: Assisted in transferring failure videos from the remote server to a local machine, troubleshooting shell globbing issues.
- How it was done: Generated rsync and SCP commands with appropriate quote escaping to handle zsh expansion conflicts.
- Impact: Enabled offline analysis of robotic task failures.
my-pc
- What was done: Implemented visual overlays for the Action-Sketcher project to display reasoning and subtask information in videos.
- How it was done: Modified draw_vp.py and run_libero_example.py to parse JSON reasoning data and burn it onto video frames using OpenCV.
- Impact: Significantly enhanced the interpretability of the model’s ‘See-Think-Sketch-Act’ loop for stakeholders.
Located and cataloged failure videos for Pi0.5 and GR00T models on the RoboCasa dataset, retrieved specific Action-Sketcher failure cases from a remote cluster, and implemented visual reasoning overlays onto evaluation videos.
Tasks
Architecture & Strategy
- ✅ Implement Reasoning Overlays for Action-Sketcher — Added code to draw subtask captions and visual sketch overlays onto evaluation videos.
- ✅ Locate Pi0.5 and GR00T Failure Videos — Found and cataloged all failure scenario videos for Pi0.5 (2,017 files) and GR00T (1,210 files) on the RoboCasa dataset.
Implementation & Fixes
- ✅ Retrieve Action-Sketcher Failure Cases — Downloaded 6 specific failure episodes from the Tianhe3 cluster for review.
Problems & Solutions
Critical Issues
1. Action-Sketcher project page mentioned visual sketches but code did not overlay them on videos.
Solution: Modified the inference script to parse the generated JSON reasoning data and use OpenCV to draw these elements as text/graphics on each frame.
Key Insight: Visual explanations are only effective if they are explicitly rendered in the output artifacts, not just stored in logs.
General Issues
2. Remote SCP failed due to zsh locally expanding the glob pattern before sending it over SSH.
Solution: Converted the command to use rsync instead, or properly quoted the remote path string to prevent local shell expansion.
Key Insight: Shell globbing in ZSH happens locally by default; quotes must be used on the remote path side of an SCP command to preserve literals.
Human vs AI Approaches
Strategic Level
Visualization Strategy Selection
| Role | Approach |
|---|---|
| Human | The user specifically asked to extract reasoning and subtask data from the project website logic and embed it into the video. |
| AI | The AI analyzed the source code for ‘draw_vp.py’ and ‘run_libero_example.py’, identified the relevant functions, and implemented a frame-by-frame overlay solution without external dependencies. |
Difference Analysis: Human provided the high-level requirement based on the project’s theoretical framework; AI translated this into specific OpenCV implementation details.
Implementation Level
SCP Globbing Behavior
| Role | Approach |
|---|---|
| Human | The user initially attempted to use standard shell wildcard syntax in an SCP command from a local ZSH terminal. |
| AI | The AI correctly identified that the local shell was expanding the path before transmission and suggested rsync or careful quoting as alternatives. |
Difference Analysis: Human focused on the intent (copying multiple files), while AI focused on the syntactic constraint of the transfer protocol vs. local shell expansion.
AI Limitations
General Limitations
- AI struggled with cross-environment shell expansion, initially suggesting a standard scp command that failed due to local terminal globbing rules.
- AI cannot directly execute commands on local user machines; it only provided scripts for the user to run locally.
Learnings
Key Learnings
- Different robotics models use different naming conventions for success/failure (e.g., _s1/_s0 vs. explicit labels); automated auditing requires parsing config/stats files to confirm semantic meaning.
Practical Learnings
- OpenCV is sufficient and lightweight for adding textual overlays and simple geometric sketches to existing video frames without needing separate compositing engines.
Conversation Summaries
RoboCasa Evaluation Audit
✅ Locating Failure Videos for Pi0.5 and GR00T 01:17:22.256 | claude_code The user asked to find failure videos for Pi0.5 and GR00T models on the RoboCasa dataset. The AI scanned the directory structure, identified naming conventions (_failure.md vs _s0), parsed stats.json for success rates, and located 2,017 Pi0.5 failures and 1,210 GR00T failures.
Remote Server File Transfer
✅ Troubleshooting Remote Video Download via SCP 01:11:42.314 | claude_code The user needed to download specific failure videos from lighthouse.egr.duke.edu. The AI assisted by generating SCP and Rsync commands, troubleshooting a ZSH globbing error that prevented the initial transfer.
Action-Sketcher Enhancement
✅ Implementing Visual Reasoning Overlays 02:29:07.173 | claude_code The user requested to add reasoning and subtask visualizations to Action-Sketcher evaluation videos. The AI modified draw_vp.py and run_libero_example.py to parse JSON reasoning data and overlay it onto video frames using OpenCV.