4 citations · 5 across the 9 of their papers we have counts for
9 papers · 1 filter
ACE-F: A Cross Embodiment Foldable System with Force Feedback for Dexterous Teleoperation
Rui Yan, Jiajian Fu, Shiqi Yang +3
Teleoperation systems are essential for efficiently collecting diverse and high-quality robot demonstration data, especially for complex, contact-rich tasks. However, current teleo…
In-N-On: Scaling Egocentric Manipulation with in-the-wild and on-task Data
Xiongyi Cai, Ri-Zhao Qiu, Geng Chen +5
Egocentric videos are a valuable and scalable data source to learn manipulation policies. However, due to significant data heterogeneity, most existing approaches utilize human dat…
HMC: Learning Heterogeneous Meta-Control for Contact-Rich Loco-Manipulation
Lai Wei, Xuanbin Peng, Ri-Zhao Qiu +3
Learning from real-world robot demonstrations holds promise for interacting with complex real-world environments. However, the complexity and variability of interaction dynamics of…
ManiFlow: A General Robot Manipulation Policy via Consistency Flow Training
Ge Yan, Jiyue Zhu, Yuquan Deng +8
This paper introduces ManiFlow, a visuomotor imitation learning policy for general robot manipulation that generates precise, high-dimensional actions conditioned on diverse visual…
GMT: General Motion Tracking for Humanoid Whole-Body Control
Zixuan Chen, Mazeyu Ji, Xuxin Cheng +3
The ability to track general whole-body motions in the real world is a useful way to build general-purpose humanoid robots. However, achieving this can be challenging due to the te…
AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control
Jialong Li, Xuxin Cheng, Tianshu Huang +3
Humanoid robots derive much of their dexterity from hyper-dexterous whole-body movements, enabling tasks that require a large operational workspace: such as picking objects off the…
Humanoid Policy ~ Human Policy
Ri-Zhao Qiu, Shiqi Yang, Xuxin Cheng +12
Training manipulation policies for humanoid robots with diverse data enhances their robustness and generalization across tasks and platforms. However, learning solely from robot de…
ACE: A Cross-Platform Visual-Exoskeletons System for Low-Cost Dexterous Teleoperation
Shiqi Yang, Minghuan Liu, Yuzhe Qin +6
Learning from demonstrations has shown to be an effective approach to robotic manipulation, especially with the recently collected large-scale robot data with teleoperation systems…
Open-TeleVision: Teleoperation with Immersive Active Visual Feedback
Xuxin Cheng, Jialong Li, Shiqi Yang +2
Teleoperation serves as a powerful method for collecting on-robot data essential for robot learning from demonstrations. The intuitiveness and ease of use of the teleoperation syst…