Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learning

• URL: http://arxiv.org/abs/2408.14063v1
• Date: Mon, 26 Aug 2024 07:44:53 GMT
• Title: Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learning
• Authors: Piotr Kicki, Davide Tateo, Puze Liu, Jonas Guenster, Jan Peters, Krzysztof Walas,
• Abstract summary: 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.
• Score: 20.158498233576143
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Trajectory planning under kinodynamic constraints is fundamental for advanced robotics applications that require dexterous, reactive, and rapid skills in complex environments. These constraints, which may represent task, safety, or actuator limitations, are essential for ensuring the proper functioning of robotic platforms and preventing unexpected behaviors. Recent advances in kinodynamic planning demonstrate that learning-to-plan techniques can generate complex and reactive motions under intricate constraints. However, these techniques necessitate the analytical modeling of both the robot and the entire task, a limiting assumption when systems are extremely complex or when constructing accurate task models is prohibitive. 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. We evaluate our approach against state-of-the-art safe reinforcement learning methods, showing that our technique, particularly when exploiting task structure, outperforms baseline methods in challenging scenarios such as planning to hit in robot air hockey. This work demonstrates the potential of our integrated approach to enhance the performance and safety of robots operating under complex kinodynamic constraints.

Related papers

• Safe Reinforcement Learning on the Constraint Manifold: Theory and Applications [21.98309272057848]
  We show how we can impose complex safety constraints on learning-based robotics systems in a principled manner. Our approach is based on the concept of the Constraint Manifold, representing the set of safe robot configurations. We demonstrate the method's effectiveness in a real-world Robot Air Hockey task.
  arXiv  Detail & Related papers  (2024-04-13T20:55:15Z)
• Modular Neural Network Policies for Learning In-Flight Object Catching with a Robot Hand-Arm System [55.94648383147838]
  We present a modular framework designed to enable a robot hand-arm system to learn how to catch flying objects. Our framework consists of five core modules: (i) an object state estimator that learns object trajectory prediction, (ii) a catching pose quality network that learns to score and rank object poses for catching, (iii) a reaching control policy trained to move the robot hand to pre-catch poses, and (iv) a grasping control policy trained to perform soft catching motions. We conduct extensive evaluations of our framework in simulation for each module and the integrated system, to demonstrate high success rates of in-flight
  arXiv  Detail & Related papers  (2023-12-21T16:20:12Z)
• Tactile Active Inference Reinforcement Learning for Efficient Robotic Manipulation Skill Acquisition [10.072992621244042]
  We propose a novel method for skill learning in robotic manipulation called Tactile Active Inference Reinforcement Learning (Tactile-AIRL) To enhance the performance of reinforcement learning (RL), we introduce active inference, which integrates model-based techniques and intrinsic curiosity into the RL process. We demonstrate that our method achieves significantly high training efficiency in non-prehensile objects pushing tasks.
  arXiv  Detail & Related papers  (2023-11-19T10:19:22Z)
• Learning and Adapting Agile Locomotion Skills by Transferring Experience [71.8926510772552]
  We propose a framework for training complex robotic skills by transferring experience from existing controllers to jumpstart learning new tasks. We show that our method enables learning complex agile jumping behaviors, navigating to goal locations while walking on hind legs, and adapting to new environments.
  arXiv  Detail & Related papers  (2023-04-19T17:37:54Z)
• Learning to Generate All Feasible Actions [4.333208181196761]
  We introduce action mapping, a novel approach that divides the learning process into two steps: first learn feasibility and subsequently, the objective. This paper focuses on the feasibility part by learning to generate all feasible actions through self-supervised querying of the feasibility model. We demonstrate the agent's proficiency in generating actions across disconnected feasible action sets.
  arXiv  Detail & Related papers  (2023-01-26T23:15:51Z)
• 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)
• Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance [71.36749876465618]
  We describe a system for vision-based dexterous manipulation that provides a "programming-free" approach for users to define new tasks. Our system includes a framework for users to define a final task and intermediate sub-tasks with image examples. experimental results with a four-finger robotic hand learning multi-stage object manipulation tasks directly in the real world.
  arXiv  Detail & Related papers  (2022-12-19T22:50:40Z)
• 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)
• From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence [113.06484656032978]
  Article argues that embodied intelligence is a key driver for the advancement of machine learning technology. We highlight challenges and opportunities specific to embodied intelligence. We propose research directions which may significantly advance the state-of-the-art in robot learning.
  arXiv  Detail & Related papers  (2021-10-28T16:04:01Z)
• Learning compositional models of robot skills for task and motion planning [39.36562555272779]
  We learn to use sensorimotor primitives to solve complex long-horizon manipulation problems. We use state-of-the-art methods for active learning and sampling. We evaluate our approach both in simulation and in the real world through measuring the quality of the selected primitive actions.
  arXiv  Detail & Related papers  (2020-06-08T20:45:34Z)
• Thinking While Moving: Deep Reinforcement Learning with Concurrent Control [122.49572467292293]
  We study reinforcement learning in settings where sampling an action from the policy must be done concurrently with the time evolution of the controlled system. Much like a person or an animal, the robot must think and move at the same time, deciding on its next action before the previous one has completed.
  arXiv  Detail & Related papers  (2020-04-13T17:49:29Z)

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.