Force Policy learns a global vision policy for free space and a local force-feedback policy that recovers an interaction frame to execute stable hybrid force-position control in contact-rich manipulation.
Vegetable Peeling: A Case Study in Constrained Dexterous Manipulation
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
An RL data generation pipeline with generalizable rewards and language annotations produces diverse synthetic datasets that improve multi-task policy generalization on three bimanual manipulation tasks.
citing papers explorer
-
Force Policy: Learning Hybrid Force-Position Control Policy under Interaction Frame for Contact-Rich Manipulation
Force Policy learns a global vision policy for free space and a local force-feedback policy that recovers an interaction frame to execute stable hybrid force-position control in contact-rich manipulation.
-
Scalable Multi-Task Data Generation via Reinforcement Learning for Language-Conditioned Bimanual Dexterous Manipulation
An RL data generation pipeline with generalizable rewards and language annotations produces diverse synthetic datasets that improve multi-task policy generalization on three bimanual manipulation tasks.