Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots

• URL: http://arxiv.org/abs/2212.02941v2
• Date: Thu, 28 Sep 2023 07:34:32 GMT
• Title: Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots
• Authors: Shamil Mamedov, Rudolf Reiter, Seyed Mahdi Basiri Azad, Joschka Boedecker, Moritz Diehl, Jan Swevers
• Abstract summary: We propose a framework for a safe approximation of NMPC using imitation learning and a predictive safety filter. Our framework significantly reduces computation time while incurring a slight loss in performance. The development of fast and safe approximate NMPC holds the potential to accelerate the adoption of flexible robots in industry.
• Score: 7.234161747563672
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Flexible robots may overcome some of the industry's major challenges, such as enabling intrinsically safe human-robot collaboration and achieving a higher load-to-mass ratio. However, controlling flexible robots is complicated due to their complex dynamics, which include oscillatory behavior and a high-dimensional state space. NMPC offers an effective means to control such robots, but its extensive computational demands often limit its application in real-time scenarios. To enable fast control of flexible robots, we propose a framework for a safe approximation of NMPC using imitation learning and a predictive safety filter. Our framework significantly reduces computation time while incurring a slight loss in performance. Compared to NMPC, our framework shows more than a eightfold improvement in computation time when controlling a three-dimensional flexible robot arm in simulation, all while guaranteeing safety constraints. Notably, our approach outperforms conventional reinforcement learning methods. The development of fast and safe approximate NMPC holds the potential to accelerate the adoption of flexible robots in industry.

Related papers

• ABNet: Attention BarrierNet for Safe and Scalable Robot Learning [58.4951884593569]
  Barrier-based method is one of the dominant approaches for safe robot learning. We propose Attention BarrierNet (ABNet) that is scalable to build larger foundational safe models in an incremental manner. We demonstrate the strength of ABNet in 2D robot obstacle avoidance, safe robot manipulation, and vision-based end-to-end autonomous driving.
  arXiv  Detail & Related papers  (2024-06-18T19:37:44Z)
• RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation [77.41969287400977]
  This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation. We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language. We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
  arXiv  Detail & Related papers  (2024-02-22T15:12:00Z)
• Domain Randomization for Robust, Affordable and Effective Closed-loop Control of Soft Robots [10.977130974626668]
  Soft robots are gaining popularity thanks to their intrinsic safety to contacts and adaptability. We show how Domain Randomization (DR) can solve this problem by enhancing RL policies for soft robots. We introduce a novel algorithmic extension to previous adaptive domain randomization methods for the automatic inference of dynamics parameters for deformable objects.
  arXiv  Detail & Related papers  (2023-03-07T18:50:00Z)
• Learning Bipedal Walking for Humanoids with Current Feedback [5.429166905724048]
  We present an approach for overcoming the sim2real gap issue for humanoid robots arising from inaccurate torque-tracking at the actuator level. Our approach successfully trains a unified, end-to-end policy in simulation that can be deployed on a real HRP-5P humanoid robot to achieve bipedal locomotion.
  arXiv  Detail & Related papers  (2023-03-07T08:16:46Z)
• Constrained Dynamic Movement Primitives for Safe Learning of Motor Skills [25.06692536893836]
  We present constrained dynamic movement primitives (CDMP) which can allow for constraint satisfaction in the robot workspace. A video showing the implementation of the proposed algorithm using different manipulators in different environments could be found here.
  arXiv  Detail & Related papers  (2022-09-28T22:59:33Z)
• Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems [79.07468367923619]
  We propose a backpropagation-free approach to robotic control through the neuro-cognitive computational framework of neural generative coding (NGC) We design an agent built completely from powerful predictive coding/processing circuits that facilitate dynamic, online learning from sparse rewards. We show that our proposed ActPC agent performs well in the face of sparse (extrinsic) reward signals and is competitive with or outperforms several powerful backprop-based RL approaches.
  arXiv  Detail & Related papers  (2022-09-19T16:49:32Z)
• High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization [42.92648945058518]
  The dependence of the movement of the robot on kinodynamic interactions becomes more pronounced at high speeds. Previous work has shown that learning the inverse kinodynamic model can be helpful for high-speed robot control. We present a new formulation for accurate, high-speed robot control that makes use of a learned forward kinodynamic (FKD) model and non-linear least squares optimization.
  arXiv  Detail & Related papers  (2022-06-16T23:52:01Z)
• Regularized Deep Signed Distance Fields for Reactive Motion Generation [30.792481441975585]
  Distance-based constraints are fundamental for enabling robots to plan their actions and act safely. We propose Regularized Deep Signed Distance Fields (ReDSDF), a single neural implicit function that can compute smooth distance fields at any scale. We demonstrate the effectiveness of our approach in representative simulated tasks for whole-body control (WBC) and safe Human-Robot Interaction (HRI) in shared workspaces.
  arXiv  Detail & Related papers  (2022-03-09T14:21:32Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning [85.13138591433635]
  The main drawbacks of input-output linearizing controllers are the need for precise dynamics models and not being able to account for input constraints. In this paper, we address both challenges for the specific case of bipedal robot control by the use of reinforcement learning techniques.
  arXiv  Detail & Related papers  (2020-04-15T18:15:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.