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
ARMOR achieves state-of-the-art performance by improving over the previous approaches by improving over the previous approaches by up to 30% on failure detection rate and up to 100% in reasoning measured through LLM fuzzy match score, demonstrating robustness to heterogeneous supervision and open-ended reasoning beyond...
Carl Qi, Xiaojie Wang, Si-Long Yong et al.· arXiv.org· 10 citations
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