Differentiable Constrained Imitation Learning for Robot Motion Planning and Control

• URL: http://arxiv.org/abs/2210.11796v2
• Date: Mon, 28 Aug 2023 09:00:50 GMT
• Title: Differentiable Constrained Imitation Learning for Robot Motion Planning and Control
• Authors: Christopher Diehl and Janis Adamek and Martin Kr\"uger and Frank Hoffmann and Torsten Bertram
• Abstract summary: We develop a framework for constraint robotic motion planning and control, as well as traffic agent simulation. We focus on mobile robot and automated driving applications. Simulated experiments of mobile robot navigation and automated driving provide evidence for the performance of the proposed method.
• Score: 0.26999000177990923
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Motion planning and control are crucial components of robotics applications like automated driving. Here, spatio-temporal hard constraints like system dynamics and safety boundaries (e.g., obstacles) restrict the robot's motions. Direct methods from optimal control solve a constrained optimization problem. However, in many applications finding a proper cost function is inherently difficult because of the weighting of partially conflicting objectives. On the other hand, Imitation Learning (IL) methods such as Behavior Cloning (BC) provide an intuitive framework for learning decision-making from offline demonstrations and constitute a promising avenue for planning and control in complex robot applications. Prior work primarily relied on soft constraint approaches, which use additional auxiliary loss terms describing the constraints. However, catastrophic safety-critical failures might occur in out-of-distribution (OOD) scenarios. This work integrates the flexibility of IL with hard constraint handling in optimal control. Our approach constitutes a general framework for constraint robotic motion planning and control, as well as traffic agent simulation, whereas we focus on mobile robot and automated driving applications. Hard constraints are integrated into the learning problem in a differentiable manner, via explicit completion and gradient-based correction. Simulated experiments of mobile robot navigation and automated driving provide evidence for the performance of the proposed method.

Related papers

• Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learning [20.158498233576143]
  Trajectory planning under kinodynamic constraints is fundamental for advanced robotics applications. Recent advances in kinodynamic planning demonstrate that learning-to-plan techniques can generate complex motions under intricate constraints. This paper addresses this limitation by combining learning-to-plan methods with reinforcement learning, resulting in a novel integration of black-box learning of motion primitives and optimization.
  arXiv  Detail & Related papers  (2024-08-26T07:44:53Z)
• Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks [29.239926645660823]
  This paper introduces a novel learning-to-plan framework that exploits the concept of constraint manifold. Our approach generates plans satisfying an arbitrary set of constraints and computes them in a short constant time, namely the inference time of a neural network. We validate our approach on two simulated tasks and in a demanding real-world scenario, where we use a Kuka LBR Iiwa 14 robotic arm to perform the hitting movement in robotic Air Hockey.
  arXiv  Detail & Related papers  (2023-01-11T06:54:11Z)
• Leveraging Sequentiality in Reinforcement Learning from a Single Demonstration [68.94506047556412]
  We propose to leverage a sequential bias to learn control policies for complex robotic tasks using a single demonstration. We show that DCIL-II can solve with unprecedented sample efficiency some challenging simulated tasks such as humanoid locomotion and stand-up.
  arXiv  Detail & Related papers  (2022-11-09T10:28:40Z)
• 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)
• Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots [60.06216976204385]
  We present an efficient motion planning framework for simultaneously solving locomotion, grasping, and contact problems. We demonstrate our proposed framework in the hardware experiments, showing that the multi-limbed robot is able to realize various motions including free-climbing at a slope angle 45deg with a much shorter planning time.
  arXiv  Detail & Related papers  (2022-07-04T13:52:10Z)
• OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation [50.59541802645156]
  Operational Space Control (OSC) has been used as an effective task-space controller for manipulation. We propose OSC for Adaptation and Robustness (OSCAR), a data-driven variant of OSC that compensates for modeling errors. We evaluate our method on a variety of simulated manipulation problems, and find substantial improvements over an array of controller baselines.
  arXiv  Detail & Related papers  (2021-10-02T01:21:38Z)
• How To Not Drive: Learning Driving Constraints from Demonstration [0.0]
  We propose a new scheme to learn motion planning constraints from human driving trajectories. The behavioral planning is responsible for high-level decision making required to follow traffic rules. The motion planner role is to generate feasible, safe trajectories for a self-driving vehicle to follow.
  arXiv  Detail & Related papers  (2021-10-01T20:47:04Z)
• Trajectory Tracking of Underactuated Sea Vessels With Uncertain Dynamics: An Integral Reinforcement Learning Approach [2.064612766965483]
  An online machine learning mechanism based on integral reinforcement learning is proposed to find a solution for a class of nonlinear tracking problems. The solution is implemented using an online value iteration process which is realized by employing means of the adaptive critics and gradient descent approaches.
  arXiv  Detail & Related papers  (2021-04-01T01:41:49Z)
• Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots [121.42930679076574]
  We present a model-free reinforcement learning framework for training robust locomotion policies in simulation. domain randomization is used to encourage the policies to learn behaviors that are robust across variations in system dynamics. We demonstrate this on versatile walking behaviors such as tracking a target walking velocity, walking height, and turning yaw.
  arXiv  Detail & Related papers  (2021-03-26T07:14:01Z)
• 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)
• Guided Constrained Policy Optimization for Dynamic Quadrupedal Robot Locomotion [78.46388769788405]
  We introduce guided constrained policy optimization (GCPO), an RL framework based upon our implementation of constrained policy optimization (CPPO) We show that guided constrained RL offers faster convergence close to the desired optimum resulting in an optimal, yet physically feasible, robotic control behavior without the need for precise reward function tuning.
  arXiv  Detail & Related papers  (2020-02-22T10:15:53Z)

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.