Contact Trajectory Prompting: In-Context Transfer of Contact-Rich Behaviors from a Single Demonstration

Key points

• In-context robot learning enables robots to acquire new behaviors from demonstrations without test-time parameter updates.1
• However, visual and kinematic specifications alone may not distinguish contact-rich behaviors that share similar motions but require different interaction intensities or patterns.1
• CTP encodes motion and contact information from a single demonstration into an ordered contact trajectory prompt that captures their temporal evolution and specifies the desired physical interaction behavior.1
• These results demonstrate that contact trajectories serve as effective behavioral specifications, extending in-context robot learning beyond motion imitation to physical interaction transfer.1

Sources (1)

• [1]Contact Trajectory Prompting: In-Context Transfer of Contact-Rich Behaviors from a Single Demonstration
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 05:58 PM

  We propose Contact Trajectory Prompting (CTP), a framework for one-shot transfer of contact-rich manipulation behaviors.

  In-context robot learning enables robots to acquire new behaviors from demonstrations without test-time parameter updates.

Extractive summary: sentences quoted from the sources.