Most robot policies, whether trained by imitation learning (IL) or reinforcement learning (RL), map observations to action values at discrete control steps and leave the continuity of physical motion to be learned implicitly. We propose ODE policy, which learns the dynamics of the action rather than the action itself....
Jin-Dou Jia, Jing-Liang Li, Kuang-Ji Zuo et al.· 0 citations
Vision-Language-Action (VLA) models are typically deployed as isolated single-agent policies. When several such agents share a workspace, they can coordinate implicitly, by observing one another, or explicitly, by exchanging messages, but it remains unclear when explicit communication becomes necessary. We introduce Co...
Ahaskar Kashyap, Jia-Wen Zong, Bo-Yu Ma et al.· 0 citations
Physically grounded robot intelligence requires robots to perceive, reason about, and regulate their interactions with the physical world. This capability is particularly critical in contact-sensitive manipulation, where successful task execution depends not only on visual perception and motion generation, but also on...
Shilin Shan, Chu-Hao Zhou, Rui-Ze Wang et al.· 0 citations
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