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Disentangling Task-Relevant Latent Dynamics for Reward Modeling
Dayou Li, Jionghao Wang, Peihao Wang +9
Reward design remains a major bottleneck for reinforcement learning in robotic manipulation. Handcrafted dense rewards can compensate for the limited guidance provided by sparse re…
RoboIRS: Inference-Time Internal Representation Steering for Generalist Robot Policies
Jiuzhou Lei, Chang Liu, Dayou Li +5
Vision-language-action (VLA) and world-action models (WAMs) often degrade under out-of-distribution task variations despite retaining partial task capability. To recover such capab…
Imagining the Sense of Touch: Touch-Informed Manipulation via Imagined Tactile Representations
Zhiyuan Zhang, Adeesh Desai, Jyun-Chi Hu +7
Tactile sensing can substantially improve contact-rich robotic manipulation, yet its practical deployment remains limited by the fragility, calibration requirements, and maintenanc…
Behavior Uncloning: Distilling Mode Redirection into Policy Weights without Inference-Time Steering
Hao Wang, Jiuzhou Lei, Dayou Li +5
Behavior-cloned policies often learn multiple behavior modes from demonstration datasets, including modes that are unsafe or otherwise undesired at deployment. For example, a polic…
ContactWorld: What Representations Matter for Vision-Tactile Latent World Models in Contact-Rich Manipulation
Zhiyuan Zhang, Pokuang Zhou, Kaidi Zhang +7
Contact-rich manipulation poses a fundamental challenge for world models: visual and tactile observations capture different aspects of physical interaction, and their utility depen…
Learning When to See and When to Feel: Adaptive Vision-Torque Fusion for Contact-Aware Manipulation
Jiuzhou Lei, Chang Liu, Yu She +2
Vision-based policies have achieved a good performance in robotic manipulation due to the accessibility and richness of visual observations. However, purely visual sensing becomes…
Learning Actionable Manipulation Recovery via Counterfactual Failure Synthesis
Dayou Li, Jiuzhou Lei, Hao Wang +6
While recent foundation models have significantly advanced robotic manipulation, these systems still struggle to autonomously recover from execution errors. Current failure-learnin…
ANGEL: A Novel Gripper for Versatile and Light-touch Fruit Harvesting
Dharmik Patel, Antonio Rafael Vazquez Pantoja, Jiuzhou Lei +3
Fruit harvesting remains predominantly a labor-intensive process, motivating the development of research for robotic grippers. Conventional rigid or vacuum-driven grippers require…
Spatio-Temporal Metric-Semantic Mapping for Persistent Orchard Monitoring: Method and Dataset
Jiuzhou Lei, Ankit Prabhu, Xu Liu +5
Monitoring orchards at the individual tree or fruit level throughout the growth season is crucial for plant phenotyping and horticultural resource optimization, such as chemical us…
SlideSLAM: Sparse, Lightweight, Decentralized Metric-Semantic SLAM for Multi-Robot Navigation
Xu Liu, Jiuzhou Lei, Ankit Prabhu +5
This paper develops a real-time decentralized metric-semantic SLAM algorithm that enables a heterogeneous robot team to collaboratively construct object-based metric-semantic maps.…