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arXiv · 2607.04652

KAM-WM: Kinematic Affordance Maps from Latent World Models for Robot Manipulation

Xinyu Shao·Keru Zhou·Guowei Huang·Yajun Gao·Tongtong Cao·Xiu Li
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Abstract

Learning manipulation from few demonstrations requires visual priors that capture not only where to interact, but also how the interaction should begin; static priors such as segmentation masks encode only the former. We present KAM-WM, a framework that extracts a coarse directional interaction cue from a frozen latent video world model without rollout or world-model fine-tuning. KAM-WM queries a Flow Matching image-to-video backbone once and interprets its single-step latent velocity as a Kinematic Affordance Map (KAM), which provides task-conditioned interaction regions and coarse motion structure. A lightweight Perceiver compresses KAM into tokens that condition a diffusion policy together with RGB observations and proprioception. Across LIBERO and RoboTwin2.0, KAM-WM reaches 90.6% average success on LIBERO and achieves 65.7% and 22.4% success rates in the Easy and Hard settings on RoboTwin2.0, respectively. Controlled comparisons against a zero-order mask prior suggest that part of the gains comes from directional information beyond spatial localization alone. These results indicate that, in the evaluated settings, a frozen video model can provide a useful first-order visual prior for control without the test-time cost of future rollout.

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Xinyu Shao, Keru Zhou, Guowei Huang, Yajun Gao, Tongtong Cao, Xiu Li. 2026-07-06. KAM-WM: Kinematic Affordance Maps from Latent World Models for Robot Manipulation. https://arxiv.org/abs/2607.04652

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