Simulation enables large-scale, low-cost robot data generation, but policies trained in simulation often fail to transfer to the real world due to the sim-to-real visual discrepancies. Existing approaches often rely on intermediate representations, which can discard rich semantic information or require additional perce...
Huang Huang, Wen-Si Ai, Zi-Yu Chen et al.· 0 citations
Human videos offer a scalable source of demonstrations for dexterous robot manipulation. However, existing human-to-simulation-to-robot (Human2Sim2Robot) pipelines rely on predefined procedures that struggle to accommodate diverse object properties and interactions, particularly those involving articulated and deformab...
You-Hui Wang, Yun-Zhu Li, Fei-Fei Li et al.· 0 citations
DemoMimic (Dexterous Motion Mimic), a policy that manipulates objects by focusing on their geometry local to the contact points to improve sim-to-real consistency, yielding a single real-world policy that transfers across objects of varying shape, scale, mass, and friction wherever local contact structure is preserved.
TrAct is proposed, a world-model-based robot decision-making framework that uses visual tracks as an intermediate interface between control and prediction, enabling more accurate world modeling and stronger robot generalization.
Zhi-Hang Cao, Howard Ji, Kevin Zhang et al.· 3 citations
This work introduces ABC, a fully open-source stack for manipulation with behavior cloning, and offers a co-training recipe that produces correlated simulation and real-world evaluation, offering a reliable proxy for ablating model-design and training decisions before costly real-world evaluation.
Arthur Allshire, Himanshu Gaurav Singh, Ritvik Singh et al.· arXiv.org· 4 citations
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