12 papers
AffordanceWAM: Affordance-Aware Joint World-Action Modeling for Robot Manipulation
Jiadi You, Qize Yu, Yue Chen +11
Generalizable robot manipulation requires predicting how a scene will evolve, identifying where interactions are feasible, and determining how to act. Action-labeled robot videos d…
BiRoAD: Learning Shared and Role-Adaptive Representations for Bimanual Manipulation
Yan Shen, Yuchen Liu, Feng Jiang +5
Bimanual manipulation requires policies that coordinate two arms while adapting their functional roles to scene geometry, object configuration, and task context. Learning such scen…
When Validation Stops Learning: Auditing Update Admission for Continual Embodied Agents
Qinzhen Ma, Ruihai Wu
Independent evaluation can reject harmful policy updates yet also prevent useful continual learning. We argue that update admission must be assessed through both error control and…
Grounding Generated Video Plans in Simulation Towards Versatile Dexterous Controllers
Tianyue Wu, Boyuan An, Shuqi Zhao +6
Generated hand-object interaction (HOI) videos provide a controllable way to propose manipulation motions. Simulation-based HOI tracking can translate such kinematic references int…
LD4WAM: Learning Latent Dynamics from Human Videos for World Action Models
Zhenhao Shen, Jiaqi Liang, Jasper Lu +11
Human video is playing an increasingly central role in training World Action Models (WAMs), owing to its diversity and low collection cost relative to teleoperated robot data. Howe…
RoboWM-Bench: A Benchmark for Evaluating World Models in Robotic Manipulation
Feng Jiang, Yang Chen, Kyle Xu +8
Recent advances in large-scale video world models have enabled increasingly realistic future prediction, raising the prospect of using generated videos as scalable supervision for…
From Seeing to Simulating: Generative High-Fidelity Simulation with Digital Cousins for Generalizable Robot Learning and Evaluation
Jasper Lu, Zhenhao Shen, Yuanfei Wang +8
Learning robust robot policies in real-world environments requires diverse data augmentation, yet scaling real-world data collection is costly due to the need for acquiring physica…
Bi-Adapt: Few-shot Bimanual Adaptation for Novel Categories of 3D Objects via Semantic Correspondence
Jinxian Zhou, Ruihai Wu, Yiwei Liu +5
Bimanual manipulation is imperative yet challenging for robots to execute complex tasks, requiring coordinated collaboration between two arms. However, existing methods for bimanua…
AdaptPNP: Integrating Prehensile and Non-Prehensile Skills for Adaptive Robotic Manipulation
Jinxuan Zhu, Chenrui Tie, Xinyi Cao +8
Non-prehensile (NP) manipulation, in which robots alter object states without forming stable grasps (for example, pushing, poking, or sliding), significantly broadens robotic manip…
Manual2Skill++: Connector-Aware General Robotic Assembly from Instruction Manuals via Vision-Language Models
Chenrui Tie, Shengxiang Sun, Yudi Lin +9
Assembly hinges on reliably forming connections between parts; yet most robotic approaches plan assembly sequences and part poses while treating connectors as an afterthought. Conn…
MetaFold: Language-Guided Multi-Category Garment Folding Framework via Trajectory Generation and Foundation Model
Haonan Chen, Junxiao Li, Ruihai Wu +9
Garment folding is a common yet challenging task in robotic manipulation. The deformability of garments leads to a vast state space and complex dynamics, which complicates precise…
Manual2Skill: Learning to Read Manuals and Acquire Robotic Skills for Furniture Assembly Using Vision-Language Models
Chenrui Tie, Shengxiang Sun, Jinxuan Zhu +7
Humans possess an extraordinary ability to understand and execute complex manipulation tasks by interpreting abstract instruction manuals. For robots, however, this capability rema…