mlsanityData to 7 Oct 2026

Learning Implicit Priors for Motion Optimization

arXiv 2204.05369 ↗First posted 11 Apr 2022cs.RO cs.AIcited by 20

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Abstract (arXiv)

In this paper, we focus on the problem of integrating Energy-based Models (EBM) as guiding priors for motion optimization. EBMs are a set of neural networks that can represent expressive probability density distributions in terms of a Gibbs distribution parameterized by a suitable energy function. Due to their implicit nature, they can easily be integrated as optimization factors or as initial sampling distributions in the motion optimization problem, making them good candidates to integrate data-driven priors in the motion optimization problem. In this work, we present a set of required modeling and algorithmic choices to adapt EBMs into motion optimization. We investigate the benefit of including additional regularizers in the learning of the EBMs to use them with gradient-based optimizers and we present a set of EBM architectures to learn generalizable distributions for manipulation tasks. We present multiple cases in which the EBM could be integrated for motion optimization and evaluate the performance of learned EBMs as guiding priors for both simulated and real robot experiments.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 28 of its 64 references that are in the map. First posted on arXiv 11 Apr 2022.

Builds on

28 of 64 references.

References · 28

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  3. 2022Generative Adversarial Networkscited by 5,603
  4. 2013Auto-Encoding Variational Bayescited by 3,980
  5. 2018Behavioral Cloning from Observationcited by 482
  6. 2018Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Reviewcited by 466
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  8. 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 400
  9. 2017Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperationcited by 286
  10. 2021How to Train Your Energy-Based Modelscited by 271
  11. 2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 201
  12. 2017Learning Sampling Distributions for Robot Motion Planningcited by 79
16 more
  1. 2020No MCMC for me: Amortized sampling for fast and stable training of energy-based modelscited by 64
  2. 2019Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Plannerscited by 62
  3. 2019Maximum Entropy Generators for Energy-Based Modelscited by 55
  4. 2017Continuous-Time Gaussian Process Motion Planning via Probabilistic Inferencecited by 49
  5. 2021STORM: An Integrated Framework for Fast Joint-Space Model-Predictive Control for Reactive Manipulationcited by 46
  6. 2020ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flowscited by 33
  7. 2019An Online Learning Approach to Model Predictive Controlcited by 29
  8. 2020Stein Variational Model Predictive Controlcited by 29
  9. 2021Composable Energy Policies for Reactive Motion Generation and Reinforcement Learningcited by 16
  10. 2020Multimodal Trajectory Optimization for Motion Planningcited by 12
  11. 2020Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensionscited by 12
  12. 2014Newton methods for k-order Markov Constrained Motion Problemscited by 6
  13. 2016Functional Gradient Motion Planning in Reproducing Kernel Hilbert Spacescited by 6
  14. 2021Entropy Regularized Motion Planning via Stein Variational Inferencecited by 5
  15. 2020Joint Sampling and Trajectory Optimization over Graphs for Online Motion Planningcited by 2
  16. 2019Online Motion Planning Over Multiple Homotopy Classes with Gaussian Process Inferencecited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 5; 2024: 5; 2025: 3; 2026: 4.

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  1. 2023Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Modelscited by 155
  2. 2022SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusioncited by 125
  3. 2024Motion Planning Diffusion: Learning and Adapting Robot Motion Planning with Diffusion Modelscited by 32
  4. 2024M2Diffuser: Diffusion-based Trajectory Optimization for Mobile Manipulation in 3D Scenescited by 26
  5. 2025FlowMP: Learning Motion Fields for Robot Planning with Conditional Flow Matchingcited by 15
  6. 2023Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Modelscited by 9
  7. 2022Hierarchical Policy Blending as Inference for Reactive Robot Controlcited by 8
  8. 2023Constrained Stein Variational Trajectory Optimizationcited by 8
  9. 2024Preconditioners for the Stochastic Training of Neural Fieldscited by 5
  10. 2022Hierarchical Policy Blending As Optimal Transportcited by 4
  11. 2023R2-Diff: Denoising by diffusion as a refinement of retrieved motion for image-based motion predictioncited by 3
  12. 2023A Unifying Variational Framework for Gaussian Process Motion Planningcited by 2
8 more
  1. 2024Global Tensor Motion Planningcited by 2
  2. 2025Generative Models From and For Sampling-Based MPC: A Bootstrapped Approach For Adaptive Contact-Rich Manipulationcited by 2
  3. 2026Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunitiescited by 1
  4. 2024Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Taskscited by 0
  5. 2025Model Tensor Planningcited by 0
  6. 2026Warm-Starting Collision-Free Model Predictive Control With Object-Centric Diffusioncited by 0
  7. 2026Referring-Aware Visuomotor Policy Learning for Closed-Loop Manipulationcited by 0
  8. 2026Learning to Plan from Random Explorationcited by 0