We introduce CNP-Flow, a flow matching framework for temporal generation that learns conditional source distributions through flow reversal. Whereas standard conditional flow matching (FM) incorporates conditioning through the vector field and draws source samples from a standard Gaussian, CNP-Flow uses a conditional n...
Kuan-Hsun Tu, Hsuan-Chi Liu, Jia-Wei Liao et al.· 0 citations
Contact-rich manipulation requires robots to sequence precise contacts, maintain stable grasps, and apply directed forces. Reinforcement learning (RL) can acquire such behaviors automatically, but its performance hinges on reward design: sparse rewards reduce the learning efficiency, while dense rewards are hard to spe...
Po-Yi Wu, Dao-Jan Chang, Shang-Ya Hsiao et al.· 0 citations
Behavioral Foundation Models (BFMs) aim to solve a wide range of downstream tasks without test-time policy learning by inferring a task vector from the reward function. While efficient, the retrieved policies are often suboptimal because of how this task vector is inferred, typically with ordinary least squares (OLS)....
Kuan-Hsun Tu, Chi-Hsun Chiang, Hsin-Wei Chen et al.· 0 citations
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