6 citations · 19 across the 18 of their papers we have counts for
8 papers · 1 filter
Translating Flow to Policy via Hindsight Online Imitation
Yitian Zheng, Zhangchen Ye, Weijun Dong +5
Recent advances in hierarchical robot systems leverage a high-level planner to propose task plans and a low-level policy to generate robot actions. This design allows training the…
MotionTrans: Human VR Data Enable Motion-Level Learning for Robotic Manipulation Policies
Chengbo Yuan, Rui Zhou, Mengzhen Liu +6
Scaling real robot data is a key bottleneck in imitation learning, leading to the use of auxiliary data for policy training. While other aspects of robotic manipulation such as ima…
KineDex: Learning Tactile-Informed Visuomotor Policies via Kinesthetic Teaching for Dexterous Manipulation
Di Zhang, Chengbo Yuan, Chuan Wen +3
Collecting demonstrations enriched with fine-grained tactile information is critical for dexterous manipulation, particularly in contact-rich tasks that require precise force contr…
FP3: A 3D Foundation Policy for Robotic Manipulation
Rujia Yang, Geng Chen, Chuan Wen +1
Following its success in natural language processing and computer vision, foundation models that are pre-trained on large-scale multi-task datasets have also shown great potential…
Data Scaling Laws in Imitation Learning for Robotic Manipulation
Fanqi Lin, Yingdong Hu, Pingyue Sheng +3
Data scaling has revolutionized fields like natural language processing and computer vision, providing models with remarkable generalization capabilities. In this paper, we investi…
General Flow as Foundation Affordance for Scalable Robot Learning
Chengbo Yuan, Chuan Wen, Tong Zhang +1
We address the challenge of acquiring real-world manipulation skills with a scalable framework. We hold the belief that identifying an appropriate prediction target capable of leve…
Any-point Trajectory Modeling for Policy Learning
Chuan Wen, Xingyu Lin, John So +4
Learning from demonstration is a powerful method for teaching robots new skills, and having more demonstration data often improves policy learning. However, the high cost of collec…
Imitation Learning from Observation with Automatic Discount Scheduling
Yuyang Liu, Weijun Dong, Yingdong Hu +4
Humans often acquire new skills through observation and imitation. For robotic agents, learning from the plethora of unlabeled video demonstration data available on the Internet ne…