ForceWAM: Decoupling Future and Response Learning from Demo and Play Data for Contact-Rich Manipulation
To fully leverage these two types of data, we introduce ForceWAM, a force world action model that decouples knowledge of task-compatible interaction outcomes from knowledge of how local actions change contact.
ProofPaper ↗
Key points
- Expert demonstrations show how to complete contact-rich tasks, but provide limited coverage of how interaction forces respond to different actions.
- Task-agnostic play provides broader experience of contact dynamics without specifying task goals.
- A world model learns from successful demonstrations to jointly predict future video and six-axis force/torque trajectories.
- A separate inverse dynamics model (IDM) learns from both task and play, mapping current observations and recorded visual-wrench futures to their associated robot actions.
Sources (1)
- [1]ForceWAM: Decoupling Future and Response Learning from Demo and Play Data for Contact-Rich ManipulationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 05:58 PM
To fully leverage these two types of data, we introduce ForceWAM, a force world action model that decouples knowledge of task-compatible interaction outcomes from knowledge of how local actions change contact.
Expert demonstrations show how to complete contact-rich tasks, but provide limited coverage of how interaction forces respond to different actions.
Extractive summary: sentences quoted from the sources.
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