19 citations · 37 across the 11 of their papers we have counts for
11 papers
VT-Refine: Learning Bimanual Assembly with Visuo-Tactile Feedback via Simulation Fine-Tuning
Binghao Huang, Jie Xu, Iretiayo Akinola +8
Humans excel at bimanual assembly tasks by adapting to rich tactile feedback -- a capability that remains difficult to replicate in robots through behavioral cloning alone, due to…
Dex1B: Learning with 1B Demonstrations for Dexterous Manipulation
Jianglong Ye, Keyi Wang, Chengjing Yuan +6
Generating large-scale demonstrations for dexterous hand manipulation remains challenging, and several approaches have been proposed in recent years to address this. Among them, ge…
SPOT: SE(3) Pose Trajectory Diffusion for Object-Centric Manipulation
Cheng-Chun Hsu, Bowen Wen, Jie Xu +5
We introduce SPOT, an object-centric imitation learning framework. The key idea is to capture each task by an object-centric representation, specifically the SE(3) object pose traj…
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…
Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learning
Runyu Ding, Yuzhe Qin, Jiyue Zhu +5
Teleoperation is a crucial tool for collecting human demonstrations, but controlling robots with bimanual dexterous hands remains a challenge. Existing teleoperation systems strugg…
Sim2Real Manipulation on Unknown Objects with Tactile-based Reinforcement Learning
Entong Su, Chengzhe Jia, Yuzhe Qin +4
Using tactile sensors for manipulation remains one of the most challenging problems in robotics. At the heart of these challenges is generalization: How can we train a tactile-base…
CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation
Jun Wang, Yuzhe Qin, Kaiming Kuang +4
We introduce CyberDemo, a novel approach to robotic imitation learning that leverages simulated human demonstrations for real-world tasks. By incorporating extensive data augmentat…
DexTouch: Learning to Seek and Manipulate Objects with Tactile Dexterity
Kang-Won Lee, Yuzhe Qin, Xiaolong Wang +1
The sense of touch is an essential ability for skillfully performing a variety of tasks, providing the capacity to search and manipulate objects without relying on visual informati…
Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing
Ying Yuan, Haichuan Che, Yuzhe Qin +6
Executing contact-rich manipulation tasks necessitates the fusion of tactile and visual feedback. However, the distinct nature of these modalities poses significant challenges. In…
GenSim: Generating Robotic Simulation Tasks via Large Language Models
Lirui Wang, Yiyang Ling, Zhecheng Yuan +6
Collecting large amounts of real-world interaction data to train general robotic policies is often prohibitively expensive, thus motivating the use of simulation data. However, exi…
Dynamic Handover: Throw and Catch with Bimanual Hands
Binghao Huang, Yuanpei Chen, Tianyu Wang +4
Humans throw and catch objects all the time. However, such a seemingly common skill introduces a lot of challenges for robots to achieve: The robots need to operate such dynamic ac…