Differentiable Motion Manifold Primitives for Reactive Motion Generation under Kinodynamic Constraints

• URL: http://arxiv.org/abs/2410.12193v2
• Date: Thu, 24 Jul 2025 11:36:31 GMT
• Title: Differentiable Motion Manifold Primitives for Reactive Motion Generation under Kinodynamic Constraints
• Authors: Yonghyeon Lee,
• Abstract summary: Differentiable Motion Manifold Primitives (DMMP) is a novel neural network architecture that encodes and generates continuous-time, differentiable trajectories.<n> Experiments on dynamic throwing with a 7-DoF robot arm demonstrate that DMMP outperforms prior methods in planning speed, task success, and constraint satisfaction.
• Score: 5.982922468400902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-time motion generation -- which is essential for achieving reactive and adaptive behavior -- under kinodynamic constraints for high-dimensional systems is a crucial yet challenging problem. We address this with a two-step approach: offline learning of a lower-dimensional trajectory manifold of task-relevant, constraint-satisfying trajectories, followed by rapid online search within this manifold. Extending the discrete-time Motion Manifold Primitives (MMP) framework, we propose Differentiable Motion Manifold Primitives (DMMP), a novel neural network architecture that encodes and generates continuous-time, differentiable trajectories, trained using data collected offline through trajectory optimizations, with a strategy that ensures constraint satisfaction -- absent in existing methods. Experiments on dynamic throwing with a 7-DoF robot arm demonstrate that DMMP outperforms prior methods in planning speed, task success, and constraint satisfaction.

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