[Submitted on 1 Oct 2026]
Title:Rethinking World-Action Model for Compositional and In-Context Robotic Manipulation
View a PDF of the paper titled Rethinking World-Action Model for Compositional and In-Context Robotic Manipulation, by Shukai Gong and 18 other authors
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Abstract:Long-horizon compositional manipulation has become increasingly important for real-world robot deployment, where a single task involves multiple coordinated subtasks. Existing world-action models (WAMs) jointly predict short-horizon visual futures and actions, but typically lack explicit subtask-level reasoning. We propose Visual Goal-conditioned Action Reasoning (ViGAR), a hierarchical framework that factorizes manipulation into a visual subgoal planner and a subgoal executor. Given the current observation and global instruction, the subgoal planner predicts a visual subgoal for the next subtask. The subgoal executor then jointly generates future visual trajectories and actions conditioned on the predicted subgoal. Both components share a pretrained world-model representation, enabling task-level planning and action generation to benefit from common physical knowledge. Moreover, our framework naturally supports in-context learning: using a global goal image as context can induce different subtask decompositions and behaviors without parameter updates. On the RoboTwin Clean2Random benchmark, ViGAR achieves 82.00% and 67.02% success rates under the Clean and Random settings, respectively, surpassing the strongest baseline by 12.86 percentage points in average success rate. Real-world robot experiments on five compositional and two in-context learning tasks further confirm the effectiveness of ViGAR.
Comments: 19 pages, 9 figures
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2610.02368 [cs.RO]
(or arXiv:2610.02368v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.02368
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Shukai Gong [view email] [v1] Thu, 1 Oct 2026 18:45:04 UTC (23,422 KB)
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