Learning Contextual Motion Constraints for Object Rearrangement Exploration
Overview
This research presents MoCo-Net, a two-stream neural network designed to predict the continuous motion ranges of articulated parts and generate obstacle heatmaps from partial-view point cloud observations. By jointly modeling intrinsic joint limits and extrinsic obstacles, the system improves feasible motion range prediction and obstacle recognition, enabling better generalization to novel objects and scenes. The resulting compact configuration space allows customized planners to efficiently explore rearrangement actions in cluttered environments.
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- arXiv Robotics — research abstractsSignalLearning Contextual Motion Constraints for Object Rearrangement Exploration
This research presents MoCo-Net, a two-stream neural network designed to predict the continuous motion ranges of articulated parts and generate obstacle heatmaps from partial-view point cloud observations. By jointly modeling intrinsic joint limits and extrinsic obstacles, the system improves feasible motion range prediction and obstacle recognition, enabling better generalization to novel objects and scenes. The resulting compact configuration space allows customized planners to efficiently explore rearrangement actions in cluttered environments.
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