Agent-as-Policy2026cc-by-4.0
YAM Agent-as-Policy — Real Dual-Arm LLM-Agent Manipulation Trials
196 real-robot sessions where an LLM agent acts directly as the policy on a bimanual YAM arm, reading camera observations and issuing Cartesian and joint commands across nine manipulation tasks. Every session includes synchronized camera streams, depth frames, 50 Hz joint trajectories, full agent event streams, and human-graded outcomes.
Downloads4
Episodes196
Likes1
Why This Matters for Physical AI
This dataset demonstrates large language models operating as end-to-end policies for real-world bimanual manipulation, providing insights into LLM reasoning, tool use, and long-horizon task planning with real robot feedback and human evaluation.
Technical Profile
- Modalities
- rgbdepthproprioceptionlanguage
- Robot Embodiments
- YAM dual-arm
- Action Space
- joint_positions, end_effector_delta
- Environment
- lab
- Task Types
- manipulationblock_stackingdie_flippingtowel_foldingpart_insertionassemblytool_use
- Episodes
- 196
- Data Format
- parquet, WebDataset, PNG
- Annotation Types
- language_instructionsreward_labelsaction_labelshuman_grading
- License
- cc-by-4.0
Access
Need custom rgb data?
Claru builds purpose-built datasets for lab applications with dense human annotations and quality assurance.
Request a Sample Pack