Learning Motion Feasibility from Point Clouds in Cluttered Environments

• URL: http://arxiv.org/abs/2606.26700v1
• Date: Thu, 25 Jun 2026 07:32:25 GMT
• Title: Learning Motion Feasibility from Point Clouds in Cluttered Environments
• Abstract summary: Motion feasibility prediction plays a central role in robotics, particularly in task and motion planning and manipulation.<n>Existing approaches for infeasibility certification are limited to low-dimensional configuration spaces and assume simplified geometric environments represented by primitive objects with known parameters.<n>We study the complementary problem of learning motion feasibility prediction directly from raw RGB-D observations for a 7-DOF manipulator operating in realistic cluttered scenes.
• Score: 1.4699455652461726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Motion feasibility prediction plays a central role in robotics, particularly in task and motion planning and manipulation. A major bottleneck for this problem in cluttered environments is that infeasible planning attempts by Sampling-based motion planners (SBMPs) can incur substantial computational cost. Also existing approaches for infeasibility certification are limited to low-dimensional configuration spaces and often assume simplified geometric environments represented by primitive objects with known parameters. We study the complementary problem of learning motion feasibility prediction directly from raw RGB-D observations for a 7-DOF manipulator operating in realistic cluttered scenes. We introduce the first large-scale benchmark for this setting, comprising 2.7M grasp feasibility labels over 88 scanned objects and 190 cluttered tabletop scenes. We benchmark three representative classifier families spanning MLP- based, volumetric-CNN, and point-cloud-based Transformer architectures under matched training conditions. Our best model, GRASPFC-PTX (a point-cloud transformer), achieves an AUROC of 0.996 on Novel objects while providing predictions significantly faster than SBMPs.

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