{"ID":5937232,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-09T04:06:17.856186522Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.04652","arxiv_id":"2607.04652","title":"KAM-WM: Kinematic Affordance Maps from Latent World Models for Robot Manipulation","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.","short_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...","url_abs":"https://arxiv.org/abs/2607.04652","url_pdf":"https://arxiv.org/pdf/2607.04652v1","authors":"[\"Xinyu Shao\",\"Keru Zhou\",\"Guowei Huang\",\"Yajun Gao\",\"Tongtong Cao\",\"Xiu Li\"]","published":"2026-07-06T04:17:15Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Diffusion Model\"]","has_code":false}