12 papers
OmniContact: Chaining Meta-Skills via Contact Flow for Generalizable Humanoid Loco-Manipulation
Runyi Yu, Xiaoyi Lin, Ji Ma +11
Learning long-horizon humanoid loco-manipulation poses a dual challenge: it requires not only the robust execution of meta-skills but also their seamless, closed-loop chaining equi…
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…
SPIDER: Scalable Physics-Informed Dexterous Retargeting
Chaoyi Pan, Changhao Wang, Haozhi Qi +7
Learning dexterous and agile policy for humanoid and dexterous hand control requires large-scale demonstrations, but collecting robot-specific data is prohibitively expensive. In c…
OTTER: A Vision-Language-Action Model with Text-Aware Visual Feature Extraction
Huang Huang, Fangchen Liu, Letian Fu +5
Vision-Language-Action (VLA) models aim to predict robotic actions based on visual observations and language instructions. Existing approaches require fine-tuning pre-trained visio…
OSMO: Open-Source Tactile Glove for Human-to-Robot Skill Transfer
Jessica Yin, Haozhi Qi, Youngsun Wi +7
Human video demonstrations provide abundant training data for learning robot policies, but video alone cannot capture the rich contact signals critical for mastering manipulation.…