Transferring robotic skills from simulation to reality requires task knowledge that remains usable across differences in perception, dynamics, and embodiment. We introduce Skill2Real, an agentic policy framework that learns executable skills through a shared application programming interface (API). A Proposer-Verifier-...
Xin-Chen He, Si-Yu Ma, Chang Yu et al.· 1 citation
Differentiable simulation is a key component in learning, control, and inverse problems, where gradients through nonlinear implicit solvers are required. Existing approaches either rely on unrolled automatic differentiation, whose memory grows with solver depth, or on equation-level implicit differentiation, which asse...
Model-based control can directly execute specified objectives, while learning can amortize such behaviors into reactive policies, making their combination a natural solution to multi-stage manipulation. We introduce Semantically UNified (SUN) Programs, typed executables that compile grounded relations into aligned opti...
Wei-Qi Wang, Zhi Li, Yuliang Lei et al.· 0 citations
ArticulateArena is proposed, a representation-invariant counterpart of Chamfer distance for articulation that compares the motions one-DOF joints induce rather than the parameters that encode them, and it is proved that the resulting quotient distance is a metric.
Yu-Meng He, Yong-Fei She, Huan-Yu Chen et al.· 0 citations
This work presents WorldAgent, an agentic framework for verification-guided physical world construction from a single natural-language prompt, without iterative user debugging, and introduces AgenticSimBench, on which WorldAgent achieves the best scores among the evaluated agent-based methods on five of seven metrics.
Caoliwen Wang, Meng-Di Wang, Yi-Ge Chen et al.· 0 citations
RoboEdit is presented, a human-to-robot video editing suite that transforms human manipulation videos into action-consistent, physically plausible robot videos with aligned 3D hand states, providing scalable, high-fidelity visual and 3D motion supervision for generalizable robot learning.
Yaowei Guo, Zeng Tao, Yuxin Jiang et al.· 1 citation
The results establish that simulation-screened task semantics can effectively amortize control into robust policies, without demonstrations or manual dense rewards, unifying symbolic planning and data-driven execution.
Wei-Qi Wang, Zhi Li, Yuliang Lei et al.· 0 citations
Agentic Real2Sim is introduced, a framework for generalized physical world modeling with vision-language agents, converting a real-world recording of object-robot interaction into a simulatable episodic twin which preserves observations, geometries, robot interactions, and object states.
This recipe for loco-manipulation generalist policies is replicated and extended to multi-object training, a first step toward loco-manipulation generalist policies at this data scale.
Omar Rayyan, Zhi Li, Max Argus et al.· 1 citation
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