mlsanityData to 14 Oct 2026

Motion Planning by Learning the Solution Manifold in Trajectory Optimization

arXiv 2107.05842 ↗First posted 13 Jul 2021cs.RO cs.LGcited by 13

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Abstract (arXiv)

The objective function used in trajectory optimization is often non-convex and can have an infinite set of local optima. In such cases, there are diverse solutions to perform a given task. Although there are a few methods to find multiple solutions for motion planning, they are limited to generating a finite set of solutions. To address this issue, we presents an optimization method that learns an infinite set of solutions in trajectory optimization. In our framework, diverse solutions are obtained by learning latent representations of solutions. Our approach can be interpreted as training a deep generative model of collision-free trajectories for motion planning. The experimental results indicate that the trained model represents an infinite set of homotopic solutions for motion planning problems.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 30 of its 69 references that are in the map. First posted on arXiv 13 Jul 2021.

Builds on

30 of 69 references.

References · 30

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18 more
  1. 2018Near-Optimal Representation Learning for Hierarchical Reinforcement Learningcited by 110
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 4; 2023: 1; 2024: 3; 2025: 3; 2026: 2.

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  1. 2024RDT-1B: a Diffusion Foundation Model for Bimanual Manipulationcited by 691
  2. 2022SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusioncited by 127
  3. 2026Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisonscited by 60
  4. 2025XR-1: Towards Versatile Vision-Language-Action Models via Learning Unified Vision-Motion Representationscited by 15
  5. 2025Exploring Embodied Multimodal Large Models: Development, Datasets, and Future Directionscited by 13
  6. 2024Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learningcited by 4
  7. 2024Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learningcited by 4
  8. 2022Tensor Train for Global Optimization Problems in Roboticscited by 2
  9. 2022Learning Solution Manifolds for Control Problems via Energy Minimizationcited by 1
  10. 2022Open-Ended Diverse Solution Discovery with Regulated Behavior Patterns for Cross-Domain Adaptationcited by 1
  11. 2026Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunitiescited by 1
  12. 2023Optimal Motion Planning using Finite Fourier Series in a Learning-based Collision Fieldcited by 0
1 more
  1. 2025A Cross-Environment and Cross-Embodiment Path Planning Framework via a Conditional Diffusion Modelcited by 0