Flow Motion Policy uses flow matching to model distributions over feasible manipulator paths, enabling best-of-N sampling with post-generation collision filtering to improve success and efficiency over prior neural and sampling-based planners.
Motion planning networks: Bridging the gap between learning-based and classical motion planners
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A primitive-based truncated diffusion model with keypoint attention encoding generates more efficient and diverse trajectories for mobile manipulators than vanilla diffusion in cluttered 3D simulations.
MfNeuPAN uses multi-frame observations and predicted future obstacle paths to enable proactive end-to-end robot navigation that improves robustness in unknown dynamic environments.
citing papers explorer
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Flow Motion Policy: Manipulator Motion Planning with Flow Matching Models
Flow Motion Policy uses flow matching to model distributions over feasible manipulator paths, enabling best-of-N sampling with post-generation collision filtering to improve success and efficiency over prior neural and sampling-based planners.
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Primitive-based Truncated Diffusion for Efficient Trajectory Generation of Differential Drive Mobile Manipulators
A primitive-based truncated diffusion model with keypoint attention encoding generates more efficient and diverse trajectories for mobile manipulators than vanilla diffusion in cluttered 3D simulations.
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MfNeuPAN: Proactive End-to-End Navigation in Dynamic Environments via Direct Multi-Frame Point Constraints
MfNeuPAN uses multi-frame observations and predicted future obstacle paths to enable proactive end-to-end robot navigation that improves robustness in unknown dynamic environments.