6 citations · 19 across the 18 of their papers we have counts for
18 papers
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…
TimeRewarder: Learning Dense Reward from Passive Videos via Frame-wise Temporal Distance
Yuyang Liu, Chuan Wen, Yihang Hu +2
Designing dense rewards is crucial for reinforcement learning (RL), yet in robotics it often demands extensive manual effort and lacks scalability. One promising solution is to vie…
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…
Predictive Inference With Fast Feature Conformal Prediction
Zihao Tang, Boyuan Wang, Chuan Wen +1
Conformal prediction is widely adopted in uncertainty quantification, due to its post-hoc, distribution-free, and model-agnostic properties. In the realm of modern deep learning, r…
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…
Can Transformers Capture Spatial Relations between Objects?
Chuan Wen, Dinesh Jayaraman, Yang Gao
Spatial relationships between objects represent key scene information for humans to understand and interact with the world. To study the capability of current computer vision syste…
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…
Seer: Language Instructed Video Prediction with Latent Diffusion Models
Xianfan Gu, Chuan Wen, Weirui Ye +2
Imagining the future trajectory is the key for robots to make sound planning and successfully reach their goals. Therefore, text-conditioned video prediction (TVP) is an essential…
Delving into Transformer for Incremental Semantic Segmentation
Zekai Xu, Mingyi Zhang, Jiayue Hou +4
Incremental semantic segmentation(ISS) is an emerging task where old model is updated by incrementally adding new classes. At present, methods based on convolutional neural network…
Predictive Inference with Feature Conformal Prediction
Jiaye Teng, Chuan Wen, Dinghuai Zhang +3
Conformal prediction is a distribution-free technique for establishing valid prediction intervals. Although conventionally people conduct conformal prediction in the output space,…
Resolving Copycat Problems in Visual Imitation Learning via Residual Action Prediction
Chia-Chi Chuang, Donglin Yang, Chuan Wen +1
Imitation learning is a widely used policy learning method that enables intelligent agents to acquire complex skills from expert demonstrations. The input to the imitation learning…
Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming
Chuan Wen, Jianing Qian, Jierui Lin +3
Across applications spanning supervised classification and sequential control, deep learning has been reported to find "shortcut" solutions that fail catastrophically under minor c…
Keyframe-Focused Visual Imitation Learning
Chuan Wen, Jierui Lin, Jianing Qian +2
Imitation learning trains control policies by mimicking pre-recorded expert demonstrations. In partially observable settings, imitation policies must rely on observation histories,…
Fighting Copycat Agents in Behavioral Cloning from Observation Histories
Chuan Wen, Jierui Lin, Trevor Darrell +2
Imitation learning trains policies to map from input observations to the actions that an expert would choose. In this setting, distribution shift frequently exacerbates the effect…