133 citations · 246 across the 24 of their papers we have counts for
27 papers
FP2: Equipping Robotic Foundation Models with Force Control
Hongjie Fang, Shirun Tang, Junjian Hu +8
Robotic foundation models (RFMs) are increasingly capable of general-purpose manipulation, yet reliable physical interaction remains challenging in contact-rich settings. We presen…
Rethinking Visual Embodiment Dependence in Visuomotor Policies
Hongjie Fang, Yuxuan Lu, Chenxi Wang +8
Visuomotor policies observe both the task scene and the acting embodiment, allowing embodiment-specific visual cues to influence action prediction. We study this phenomenon as visu…
HINT: Human-Intent Inception for Long-Horizon Robot Manipulation
Mingyu Mei, Haojie Xu, Shihao Jin +9
Humans can perform complex manipulations given a simple intent through an overall instruction, while continuously adapting to evolving visual observations. However, current vision-…
ChronoFlow-Policy: Unifying Past-Current-Future Interaction Flow in Visuomotor Policy Learning
Bokai Lin, Yifu Xu, Xinyu Zhan +6
Visual signals play a crucial role in policy learning by enabling models to capture object motion and interaction dynamics. Just as humans reason about actions using both past expe…
MATE: Asynchronous Multi-Rate Diffusion Policy Composition
Zihao He, Hongjie Fang, Shirun Tang +2
Diffusion policies have shown strong potential for robotic imitation learning, and recent extensions incorporate additional modalities to improve manipulation performance. However,…
AnyDexRT: Calibration-Efficient Dexterous Hand Retargeting with Few-Shot Human Guidance
Chenxi Wang, Ying Feng, Hongjie Fang +4
Teleoperation is a key interface for controlling dexterous robotic hands and collecting demonstrations for imitation learning. Its effectiveness largely depends on kinematic retarg…
X-Imitator: Spatial-Aware Imitation Learning via Bidirectional Action-Pose Interaction
Kai Xiong, Hongjie Fang, Lixin Yang +1
Effectively handling the interplay between spatial perception and action generation remains a critical bottleneck in robotic manipulation. Existing methods typically treat spatial…
LIDEA: Human-to-Robot Imitation Learning via Implicit Feature Distillation and Explicit Geometry Alignment
Yifu Xu, Bokai Lin, Xinyu Zhan +4
Scaling up robot learning is hindered by the scarcity of robotic demonstrations, whereas human videos offer a vast, untapped source of interaction data. However, bridging the embod…
Learning Dexterous Manipulation with Quantized Hand State
Ying Feng, Hongjie Fang, Yinong He +5
Dexterous robotic hands enable robots to perform complex manipulations that require fine-grained control and adaptability. Achieving such manipulation is challenging because the hi…
History-Aware Visuomotor Policy Learning via Point Tracking
Jingjing Chen, Hongjie Fang, Chenxi Wang +2
Many manipulation tasks require memory beyond the current observation, yet most visuomotor policies rely on the Markov assumption and thus struggle with repeated states or long-hor…
Knowledge-Driven Imitation Learning: Enabling Generalization Across Diverse Conditions
Zhuochen Miao, Jun Lv, Hongjie Fang +2
Imitation learning has emerged as a powerful paradigm in robot manipulation, yet its generalization capability remains constrained by object-specific dependencies in limited expert…
SIME: Enhancing Policy Self-Improvement with Modal-level Exploration
Yang Jin, Jun Lv, Wenye Yu +3
Self-improvement requires robotic systems to initially learn from human-provided data and then gradually enhance their capabilities through interaction with the environment. This i…
Dense Policy: Bidirectional Autoregressive Learning of Actions
Yue Su, Xinyu Zhan, Hongjie Fang +5
Mainstream visuomotor policies predominantly rely on generative models for holistic action prediction, while current autoregressive policies, predicting the next token or chunk, ha…
AirExo-2: Scaling up Generalizable Robotic Imitation Learning with Low-Cost Exoskeletons
Hongjie Fang, Chenxi Wang, Yiming Wang +11
Scaling up robotic imitation learning for real-world applications requires efficient and scalable demonstration collection methods. While teleoperation is effective, it depends on…
AnyDexGrasp: General Dexterous Grasping for Different Hands with Human-level Learning Efficiency
Hao-Shu Fang, Hengxu Yan, Zhenyu Tang +3
We introduce an efficient approach for learning dexterous grasping with minimal data, advancing robotic manipulation capabilities across different robotic hands. Unlike traditional…
FoAR: Force-Aware Reactive Policy for Contact-Rich Robotic Manipulation
Zihao He, Hongjie Fang, Jingjing Chen +2
Contact-rich tasks present significant challenges for robotic manipulation policies due to the complex dynamics of contact and the need for precise control. Vision-based policies o…
Motion Before Action: Diffusing Object Motion as Manipulation Condition
Yue Su, Xinyu Zhan, Hongjie Fang +3
Inferring object motion representations from observations enhances the performance of robotic manipulation tasks. This paper introduces a new paradigm for robot imitation learning…
CAGE: Causal Attention Enables Data-Efficient Generalizable Robotic Manipulation
Shangning Xia, Hongjie Fang, Cewu Lu +1
Generalization in robotic manipulation remains a critical challenge, particularly when scaling to new environments with limited demonstrations. This paper introduces CAGE, a novel…
Towards Effective Utilization of Mixed-Quality Demonstrations in Robotic Manipulation via Segment-Level Selection and Optimization
Jingjing Chen, Hongjie Fang, Hao-Shu Fang +1
Data is crucial for robotic manipulation, as it underpins the development of robotic systems for complex tasks. While high-quality, diverse datasets enhance the performance and ada…
Graspness Discovery in Clutters for Fast and Accurate Grasp Detection
Chenxi Wang, Hao-Shu Fang, Minghao Gou +3
Efficient and robust grasp pose detection is vital for robotic manipulation. For general 6 DoF grasping, conventional methods treat all points in a scene equally and usually adopt…
RISE: 3D Perception Makes Real-World Robot Imitation Simple and Effective
Chenxi Wang, Hongjie Fang, Hao-Shu Fang +1
Precise robot manipulations require rich spatial information in imitation learning. Image-based policies model object positions from fixed cameras, which are sensitive to camera vi…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
AirExo: Low-Cost Exoskeletons for Learning Whole-Arm Manipulation in the Wild
Hongjie Fang, Hao-Shu Fang, Yiming Wang +5
While humans can use parts of their arms other than the hands for manipulations like gathering and supporting, whether robots can effectively learn and perform the same type of ope…
Flexible Handover with Real-Time Robust Dynamic Grasp Trajectory Generation
Gu Zhang, Hao-Shu Fang, Hongjie Fang +1
In recent years, there has been a significant effort dedicated to developing efficient, robust, and general human-to-robot handover systems. However, the area of flexible handover…