mlsanityData to 7 Oct 2026

Differentiable Gaussian Process Motion Planning

arXiv 1907.09591 ↗First posted 22 Jul 2019cs.ROcited by 27

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Abstract (arXiv)

Modern trajectory optimization based approaches to motion planning are fast, easy to implement, and effective on a wide range of robotics tasks. However, trajectory optimization algorithms have parameters that are typically set in advance (and rarely discussed in detail). Setting these parameters properly can have a significant impact on the practical performance of the algorithm, sometimes making the difference between finding a feasible plan or failing at the task entirely. We propose a method for leveraging past experience to learn how to automatically adapt the parameters of Gaussian Process Motion Planning (GPMP) algorithms. Specifically, we propose a differentiable extension to the GPMP2 algorithm, so that it can be trained end-to-end from data. We perform several experiments that validate our algorithm and illustrate the benefits of our proposed learning-based approach to motion planning.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jul 2019.

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Citing papers per year: 2020: 1; 2021: 5; 2022: 7; 2023: 8; 2024: 4; 2025: 2.

27 papers.

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  1. 2021Habitat 2.0: Training Home Assistants to Rearrange their Habitatcited by 501
  2. 2023Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Modelscited by 155
  3. 2022Tutorial on amortized optimizationcited by 83
  4. 2022Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Drivingcited by 71
  5. 2022Theseus: A Library for Differentiable Nonlinear Optimizationcited by 64
  6. 2021Differentiable SLAM-net: Learning Particle SLAM for Visual Navigationcited by 34
  7. 2022Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigationcited by 27
  8. 2021Differentiable Spatial Planning using Transformerscited by 25
  9. 2021Exploring Imitation Learning for Autonomous Driving with Feedback Synthesizer and Differentiable Rasterizationcited by 22
  10. 2024Hybrid-Prediction Integrated Planning for Autonomous Drivingcited by 18
  11. 2020APPLD: Adaptive Planner Parameter Learning from Demonstrationcited by 17
  12. 2025Co-MTP: A Cooperative Trajectory Prediction Framework with Multi-Temporal Fusion for Autonomous Drivingcited by 14
15 more
  1. 2022Coarse-to-Fine Q-attention with Learned Path Rankingcited by 13
  2. 2023TaskMet: Task-Driven Metric Learning for Model Learningcited by 11
  3. 2022Learning Sampling Distributions for Model Predictive Controlcited by 10
  4. 2022Learning to Optimize in Model Predictive Controlcited by 10
  5. 2023Occupancy Prediction-Guided Neural Planner for Autonomous Drivingcited by 8
  6. 2023Revisiting Implicit Differentiation for Learning Problems in Optimal Controlcited by 8
  7. 2021Robot Program Parameter Inference via Differentiable Shadow Program Inversioncited by 5
  8. 2024DiffTORI: Differentiable Trajectory Optimization for Deep Reinforcement and Imitation Learningcited by 5
  9. 2023CaRE: Finding Root Causes of Configuration Issues in Highly-Configurable Robotscited by 4
  10. 2025Differentiable Model Predictive Control on the GPUcited by 4
  11. 2023Decision-Oriented Learning with Differentiable Submodular Maximization for Vehicle Routing Problemcited by 2
  12. 2024From Variance to Veracity: Unbundling and Mitigating Gradient Variance in Differentiable Bundle Adjustment Layerscited by 1
  13. 2024Shadow Program Inversion with Differentiable Planning: A Framework for Unified Robot Program Parameter and Trajectory Optimizationcited by 1
  14. 2023Reinforcement Learning in a Safety-Embedded MDP with Trajectory Optimizationcited by 0
  15. 2023Optimal Motion Planning using Finite Fourier Series in a Learning-based Collision Fieldcited by 0