arXiv:2609.34182v2 Announce Type: replace-cross Abstract: Dexterous manipulation requires tactile feedback. However, robot tactile demonstrations are difficult to scale,because dexterous-hand teleope...
arXiv:2607. 04591v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation by integrating visual perception, language understanding, and robot action generation.
arXiv:2609.16683v1 Announce Type: cross Abstract: Learning humanoid-object interaction requires coordinating whole-body balance, locomotion, and dexterous hand contact to control both robot and objec...
Learning humanoid-object interaction requires coordinating whole-body balance, locomotion, and dexterous hand contact to control both robot and object motion. Human demonstrations provide examples of...
arXiv:2606. 15148v1 Announce Type: cross Abstract: Inverse kinematics (IK) remains a critical bottleneck for real-time robot manipulation.
arXiv:2609.39388v1 Announce Type: cross Abstract: Mobile manipulation requires precise navigation to a manipulation-ready pose followed by reliable object interaction. These two stages differ in acti...
Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation by integrating visual perception, language understanding, and robot action generation. Existing research has primarily focused on improving model architectures, training strategies, and dataset scale, while little attention has been paid to how demonstrations are collected and organized.
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:2602. 13197v2 Announce Type: replace-cross Abstract: The ability to learn manipulation skills by watching videos of humans has the potential to unlock a new source of highly scalable data for robot learning.
arXiv:2606. 08530v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models achieve strong benchmark performance but still struggle in real-world deployment with unseen objects, background shifts, and different robot embodiments.
MAVP (Map-Aware Visuomotor Policies) is a framework that enhances mobile manipulation by predicting explicit base-pose targets and tracking them with localisation feedback. It reconstructs a static map from teleoperated demonstrations, expressing base trajectories in a shared map frame to provide consistent spatial supervision. During execution, the policy uses RGB observations, joint states, and the robot’s current map-frame base pose to jointly predict target base poses, arm actions, and gripper actions, while a low‑level controller corrects deviations using feedforward motion and pose error feedback. Pose‑noise augmentation during training further improves robustness, and MAVP outperforms unanchored velocity control across six real‑world tasks and three policy families.
arXiv:2609.37181v1 Announce Type: cross Abstract: Human demonstrations capture diverse scenes and rich whole-body skills without requiring robot teleoperation. Prior work on egocentric transfer has e...