arXiv:2609.07534v2 Announce Type: replace-cross Abstract: Contact-rich assembly remains challenging because it requires submillimeter spatial accuracy and reliable interpretation of forces during sus...
Action-conditioned latent world models predict future visual representations, enabling zero-shot goal-conditioned robot planning and control. However, their predictions for fine-grained spatial and ro...
arXiv:2609.10506v1 Announce Type: cross Abstract: Action-conditioned latent world models predict future visual representations, enabling zero-shot goal-conditioned robot planning and control. However...
ForceFlow is a force-aware reactive framework that uses flow matching to improve contact-rich manipulation. It fuses force signals asymmetrically, treats force as a global regulator, and employs a joint prediction paradigm to couple force and motion. The approach splits tasks into a vision-dominant localization stage and a touch-dominant execution stage, using a Vision-to-Force handover to separate spatial generalization from contact regulation.
Universal Manipulation Interface (UMI) enables scalable real-world robot data collection without hardware-specific teleoperation, yet leveraging UMI data to train large-scale Vision-Language-Action (VLA) models remains fundamentally challenging. We identify two critical mismatches: wrist-mounted fisheye views, with severe radial distortion and local gripper-centric perspectives, are out-of-distribution for pretrained VLMs; and human-collected trajectories frequently violate kinematic limits, incur collisions, or exceed controller bandwidth, teaching VLA policies physically infeasible actions.
arXiv:2606. 04708v1 Announce Type: cross Abstract: Universal Manipulation Interface (UMI) enables scalable real-world robot data collection without hardware-specific teleoperation, yet leveraging UMI data to train large-scale Vision-Language-Action (VLA) models remains fundamentally challenging.
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:2512. 10946v2 Announce Type: replace-cross Abstract: Human-level contact-rich manipulation relies on the distinct roles of two key modalities: vision provides spatially rich but temporally slow global context, while force sensing captures rapid local contact dynamics.
arXiv:2607. 01410v1 Announce Type: cross Abstract: Sim2real transfer for robot policy learning suffers due to mismatch between simulation and reality.
arXiv:2606. 24450v1 Announce Type: cross Abstract: Perceiving physical contact is fundamental to dexterous manipulation.
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:2606. 06218v1 Announce Type: cross Abstract: A policy tuned for one robot often behaves differently on another, whether due to the sim-to-real gap, unknown payloads, or the differing dynamics of two instances of the same robot.