Generalist robot policies offer strong behavioral priors, but achieving deployment-level reliability and throughput remains difficult. Reinforcement learning (RL) can improve these policies, yet its effectiveness is constrained by the cost of online interaction. Existing methods adapt frozen flow-matching policies by t...
Carl Qi, Wei-Jun Dong, Xiao-Jie Wang et al.· 0 citations
Motivated by the human ability to integrate experience across language, perception, and action, we introduce UniWALM, a unified world-action-language model that learns video, action, and language in a single shared representation, where each modality is both a conditioning signal and a supervision target. We ask: can j...
Rahul Sajnani, D. Romeres, Ming-Yuan Zhang et al.· 0 citations
GlanceWAM is introduced, which decouples imagination from control within a single video DiT: an asynchronous proposer glances ahead on a slow clock to imagine a single lookahead frame seconds into the future in the background, while an action head decodes action chunks at control rate purely in latent space without blo...
Lin-Han Wang, Zi-Jian An, Mingyuan Zhang et al.· 3 citations
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