Newest papers

Plausible Future, Wrong Choice: Steering World-Action Models toward Intended Future

Zhuohao Li, Jiawei He, Yuxuan Yan, Lang Zhou, Yinghao Li, Yong Li, Tianyu Zhang, Yi-Yang Song, Xirui Wu andYi-Lin Wei, Fating Hong, Qing Zhang, Zhilin Zhao, Jingdong Wang, Wei-Shi Zheng · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28431 · cited by 0 works (OpenAlex) · open-access PDF

Abstract

World-Action Models (WAMs) have emerged as a promising paradigm for embodied generalist policies by jointly modeling future world evolution and robot actions across diverse manipulation tasks. However, representing diverse futures is only the first step: a capable WAM must also generate futures that match the requirements of the current task and execution progress. In long-horizon manipulation where similar observation-action segments occur across different tasks and stages, existing approaches may generate locally plausible yet misdirected rollouts to drift toward wrong task stage, i.e., a phenomenon we term Stage Confusion. To address this problem, we propose TaskWAM, a closed-loop, stage-aware framework that represents fine-grained task requirements with Mode Tokens and tracks execution progress with State Tokens, forming a structured task state to steer world--action generation toward intended future. We further introduce StageBench, a controlled multi-stage manipulation benchmark with parameterized stage variations and automated evaluation.Extensive experiments on both simulation benchmarks and real-world tasks demonstrate that TaskWAM effectively reduces confusion and achieves competitive performance with state-of-the-art methods. Moreover, TaskWAM provides strong plug-and-play transfer across heterogeneous WAM architectures and robot embodiments, including single-arm robots, dual-arm grippers, dexterous hands, and humanoid robots.

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Topics: Computer science, Task (project management), Benchmark (surveying), Robot, Reachability, Artificial intelligence

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