arXiv:2509. 19696v4 Announce Type: replace-cross Abstract: Learning-based methods excel at robot motion generation but remain limited in contact-rich physical interaction.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2606. 14218v1 Announce Type: cross Abstract: For robots to work safely in household environments, they need to be compliant and react to torque and force feedback during contact.
arXiv:2607. 00033v1 Announce Type: cross Abstract: Dexterous robot manipulation can benefit from the abundance of human demonstrations, but transferring such demonstrations to robot policies remains challenging.
arXiv:2607. 09218v1 Announce Type: cross Abstract: Whole-arm manipulation involves direct contact with the environment while the robot completes a task by distributing contact across multiple links as contacts form, slide, and break.
arXiv:2607. 09218v2 Announce Type: replace-cross Abstract: Whole-arm manipulation involves direct contact with the environment while the robot completes a task by distributing contact across multiple links as contacts form, slide, and break.
arXiv:2608. 03103v1 Announce Type: cross Abstract: Diffusion policies have shown strong performance in learning complex, multi-modal behaviors for robotic manipulation.
arXiv:2607. 12105v1 Announce Type: cross Abstract: In-hand manipulation without external sensing is challenging due to uncertainties from finger-object contacts and disturbances by gravity.