Predict in Chunks, Revise in Spans: History-conditioned Action Regeneration for Robotic Manipulation
We introduce Contextual Masked Action Policy (CMAP), a visuomotor policy that refines continuous action chunks through history-conditioned local regeneration.
ProofPaper ↗
Key points
- Action chunking improves temporal coherence in visuomotor imitation learning by jointly predicting sequences of future actions.
- Specifically, CMAP introduces a history-conditioned action tape that combines executed commands with masked future positions to produce coarse action predictions and per-step uncertainty scores.
- An uncertainty-guided span regeneration mechanism then selects a high-uncertainty contiguous span and reconstructs it using the executed history and retained future predictions as context.
- By jointly reconstructing neighboring actions, CMAP models local temporal dependencies while preserving all predictions outside the selected span.
Sources (1)
- [1]Predict in Chunks, Revise in Spans: History-conditioned Action Regeneration for Robotic ManipulationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 12:33 PM
We introduce Contextual Masked Action Policy (CMAP), a visuomotor policy that refines continuous action chunks through history-conditioned local regeneration.
Action chunking improves temporal coherence in visuomotor imitation learning by jointly predicting sequences of future actions.
Extractive summary: sentences quoted from the sources.