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Data-efficient, Explainable and Safe Box Manipulation: Illustrating the Advantages of Physical Priors in Model-Predictive Control

arXiv 2303.01563 ↗First posted 2 Mar 2023cs.RO cs.LGcited by 0

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Abstract (arXiv)

Model-based RL/control have gained significant traction in robotics. Yet, these approaches often remain data-inefficient and lack the explainability of hand-engineered solutions. This makes them difficult to debug/integrate in safety-critical settings. However, in many systems, prior knowledge of environment kinematics/dynamics is available. Incorporating such priors can help address the aforementioned problems by reducing problem complexity and the need for exploration, while also facilitating the expression of the decisions taken by the agent in terms of physically meaningful entities. Our aim with this paper is to illustrate and support this point of view via a case-study. We model a payload manipulation problem based on a real robotic system, and show that leveraging prior knowledge about the dynamics of the environment in an MPC framework can lead to improvements in explainability, safety and data-efficiency, leading to satisfying generalization properties with less data.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 23 references that are in the map. First posted on arXiv 2 Mar 2023.

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14 of 23 references.

References · 14

  1. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  2. 2020Meta-Learning in Neural Networks: A Surveycited by 944
  3. 2019Model-Based Reinforcement Learning for Ataricited by 495
  4. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 388
  5. 2020Planning to Explore via Self-Supervised World Modelscited by 365
  6. 2017Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuningcited by 288
  7. 2021State Entropy Maximization with Random Encoders for Efficient Explorationcited by 107
  8. 2020Sample-efficient Cross-Entropy Method for Real-time Planningcited by 90
  9. 2021Explainable Artificial Intelligence Approaches: A Surveycited by 40
  10. 2020Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learningcited by 27
  11. 2020Sim-to-Real Transfer with Incremental Environment Complexity for Reinforcement Learning of Depth-Based Robot Navigationcited by 13
  12. 2022RAMP-Net: A Robust Adaptive MPC for Quadrotors via Physics-informed Neural Networkcited by 12
2 more
  1. 2020LBGP: Learning Based Goal Planning for Autonomous Following in Frontcited by 4
  2. 2022Adaptive Asynchronous Control Using Meta-learned Neural Ordinary Differential Equationscited by 2

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