Decentralized Deep Reinforcement Learning for a Distributed and Adaptive Locomotion Controller of a Hexapod Robot

• URL: http://arxiv.org/abs/2005.11164v1
• Date: Thu, 21 May 2020 11:40:37 GMT
• Title: Decentralized Deep Reinforcement Learning for a Distributed and Adaptive Locomotion Controller of a Hexapod Robot
• Authors: Malte Schilling, Kai Konen, Frank W. Ohl, Timo Korthals
• Abstract summary: We propose a decentralized organization as found in insect motor control for coordination of different legs. A concurrent local structure is able to learn better walking behavior.
• Score: 0.6193838300896449
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Locomotion is a prime example for adaptive behavior in animals and biological control principles have inspired control architectures for legged robots. While machine learning has been successfully applied to many tasks in recent years, Deep Reinforcement Learning approaches still appear to struggle when applied to real world robots in continuous control tasks and in particular do not appear as robust solutions that can handle uncertainties well. Therefore, there is a new interest in incorporating biological principles into such learning architectures. While inducing a hierarchical organization as found in motor control has shown already some success, we here propose a decentralized organization as found in insect motor control for coordination of different legs. A decentralized and distributed architecture is introduced on a simulated hexapod robot and the details of the controller are learned through Deep Reinforcement Learning. We first show that such a concurrent local structure is able to learn better walking behavior. Secondly, that the simpler organization is learned faster compared to holistic approaches.

Related papers

• Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control [112.66677641636299]
  This paper presents a study on using deep reinforcement learning to create dynamic locomotion controllers for bipedal robots. We develop a general control solution that can be used for a range of dynamic bipedal skills, from periodic walking and running to aperiodic jumping and standing. This work pushes the limits of agility for bipedal robots through extensive real-world experiments.
  arXiv  Detail & Related papers  (2024-01-30T10:48:43Z)
• Hierarchical generative modelling for autonomous robots [8.023920215148486]
  We show how a humanoid robot can autonomously complete a complex task that requires a holistic use of locomotion, manipulation, and grasping. Specifically, we demonstrate the ability of a humanoid robot that can retrieve and transport a box, open and walk through a door to reach the destination, approach and kick a football, while showing robust performance in presence of body damage and ground irregularities.
  arXiv  Detail & Related papers  (2023-08-15T13:51:03Z)
• Decentralized Motor Skill Learning for Complex Robotic Systems [5.669790037378093]
  We propose a Decentralized motor skill (DEMOS) learning algorithm to automatically discover motor groups that can be decoupled from each other. Our method improves the robustness and generalization of the policy without sacrificing performance. Experiments on quadruped and humanoid robots demonstrate that the learned policy is robust against local motor malfunctions and can be transferred to new tasks.
  arXiv  Detail & Related papers  (2023-06-30T05:55:34Z)
• 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)
• Hierarchical Decentralized Deep Reinforcement Learning Architecture for a Simulated Four-Legged Agent [0.0]
  In nature, control of movement happens in a hierarchical and decentralized fashion. We present a novel decentral, hierarchical architecture to control a simulated legged agent.
  arXiv  Detail & Related papers  (2022-09-21T07:55:33Z)
• A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning [86.06110576808824]
  Deep reinforcement learning is a promising approach to learning policies in uncontrolled environments. Recent advancements in machine learning algorithms and libraries combined with a carefully tuned robot controller lead to learning quadruped in only 20 minutes in the real world.
  arXiv  Detail & Related papers  (2022-08-16T17:37:36Z)
• Versatile modular neural locomotion control with fast learning [6.85316573653194]
  Legged robots have significant potential to operate in highly unstructured environments. Currently, controllers must be either manually designed for specific robots or automatically designed via machine learning methods. We propose a simple yet versatile modular neural control structure with fast learning.
  arXiv  Detail & Related papers  (2021-07-16T12:12:28Z)
• Neural Dynamic Policies for End-to-End Sensorimotor Learning [51.24542903398335]
  The current dominant paradigm in sensorimotor control, whether imitation or reinforcement learning, is to train policies directly in raw action spaces. We propose Neural Dynamic Policies (NDPs) that make predictions in trajectory distribution space. NDPs outperform the prior state-of-the-art in terms of either efficiency or performance across several robotic control tasks.
  arXiv  Detail & Related papers  (2020-12-04T18:59:32Z)
• 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)
• Learning Agile Robotic Locomotion Skills by Imitating Animals [72.36395376558984]
  Reproducing the diverse and agile locomotion skills of animals has been a longstanding challenge in robotics. We present an imitation learning system that enables legged robots to learn agile locomotion skills by imitating real-world animals.
  arXiv  Detail & Related papers  (2020-04-02T02:56:16Z)

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.