Vision-language-action (VLA) policies can execute many tasks from standard initial states, yet a new instruction may fail after another task has altered the robot's physical state. We study instruction switching, where a new task is issued during or after the execution of a different one. We observe that a target task...
Qing-Zi Wang, Kai Feng, Guang-Yao Shi et al.· 0 citations
STAIRCASE POLICY is introduced, a streaming inference and training framework that turns a flow-matching VLA into a JEPA-style WAM and partitions a large action chunk into sub-chunks at staggered denoising stages, enabling long-horizon execution without repeated full policy inference.
Guoheng Sun, Chen Chen, Jin Wang et al.· 0 citations
Neuro-symbolic computer use is introduced, in which a recurring workflow is executed by a learned policy rather than re-derived by an agent on each run, and a pre-action verifier guards each state-mutating step at deployment.
Hyewon Suh, Than Minh Nguyen, Chih-Lun Lee et al.· 0 citations
Experiments show that UEWM provides reliable and transferable reward signals across domains, and that WMG-RL enables a compact 1.7B student policy to match or surpass much larger LLMs on downstream recommendation tasks.
The RoboSynChallenge competition introduces a unified benchmark to evaluate and advance the generalizability of manipulation policies across a spectrum of tasks, environments, and difficulty levels and aims to foster the development of broadly capable, data-efficient, and adaptable manipulation systems, thereby paving...
Runyi Zhao, Rui-Han Wu, Cheng-Kun Li et al.· 0 citations
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