Agent-as-Policy2026cc-by-4.0
Agent as Policy — Real Dual-Arm LLM-Agent Manipulation Trials
162 real-robot trials where an LLM agent acts directly as the policy on a bimanual YAM arm setup, reading camera observations and issuing Cartesian and joint commands across ten manipulation tasks.
Downloads30
Episodes162
Likes2
Why This Matters for Physical AI
This dataset provides a comprehensive record of LLM agents directly controlling dual-arm manipulation in real-world settings, enabling research into how language models can serve as policies for long-horizon robotic tasks with full step-by-step reasoning transparency.
Technical Profile
- Modalities
- rgbdepthproprioceptionlanguage
- Robot Embodiments
- YAM bimanual arm
- Action Space
- joint_positions, end_effector_delta
- Environment
- lab
- Task Types
- manipulationblock_stackingdie_flippingtowel_foldingpart_insertionassemblythrowingtool_use
- Episodes
- 162
- Data Format
- parquet, WebDataset
- Annotation Types
- language_instructionsreward_labelsaction_labels
- License
- cc-by-4.0
Access
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