21 papers
Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning
Shilin Shan, Chuhao Zhou, Ruize Wang +30
Physically grounded robot intelligence requires robots to perceive, reason about, and regulate their interactions with the physical world. This capability is particularly critical…
Scalable Behavior Cloning with Open Data, Training, and Evaluation
Arthur Allshire, Himanshu Gaurav Singh, Ritvik Singh +15
We introduce ABC, a fully open-source stack for manipulation with behavior cloning. At its core is ABC-130K: the largest open-source teleoperation dataset to date, featuring 3,500…
ForceBand: Learning Forceful Manipulation with sEMG
Botao He, Zhi Wang, Linna Kuang +8
Human demonstrations are a scalable data source for learning robot manipulation policies. However, common sources of human demonstration data, such as motion-capture trajectories a…
PTLD: Sim-to-real Privileged Tactile Latent Distillation for Dexterous Manipulation
Rosy Chen, Mustafa Mukadam, Michael Kaess +4
Tactile dexterous manipulation is essential to automating complex household tasks, yet learning effective control policies remains a challenge. While recent work has relied on imit…
Do as I Do: Dexterous Manipulation Data from Everyday Human Videos
Bhawna Paliwal, Haritheja Etukuru, William Liang +3
How can we scalably generate data for robotic manipulation, especially on human-like platforms such as dexterous multi-fingered hands? Learning from human videos has recently emerg…
Contrastive Action-Image Pre-training for Visuomotor Control
Yuvan Sharma, Dantong Niu, Anirudh Pai +16
Existing vision encoders for robotics face a fundamental bottleneck: robotic datasets lack the scale necessary for large-scale pre-training. Prior work circumvents this data scarci…