Training Directional Locomotion for Quadrupedal Low-Cost Robotic Systems via Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2503.11059v1
• Date: Fri, 14 Mar 2025 03:53:01 GMT
• Title: Training Directional Locomotion for Quadrupedal Low-Cost Robotic Systems via Deep Reinforcement Learning
• Authors: Peter Böhm, Archie C. Chapman, Pauline Pounds,
• Abstract summary: We present Deep Reinforcement Learning training of directional locomotion for low-cost quadpedalru robots in the real world.<n>We exploit randomization of heading that the robot must follow to foster exploration of action-state transitions.<n>Changing the heading in episode resets to current yaw plus a random value drawn from a normal distribution yields policies able to follow complex trajectories.
• Score: 4.669957449088593
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this work we present Deep Reinforcement Learning (DRL) training of directional locomotion for low-cost quadrupedal robots in the real world. In particular, we exploit randomization of heading that the robot must follow to foster exploration of action-state transitions most useful for learning both forward locomotion as well as course adjustments. Changing the heading in episode resets to current yaw plus a random value drawn from a normal distribution yields policies able to follow complex trajectories involving frequent turns in both directions as well as long straight-line stretches. By repeatedly changing the heading, this method keeps the robot moving within the training platform and thus reduces human involvement and need for manual resets during the training. Real world experiments on a custom-built, low-cost quadruped demonstrate the efficacy of our method with the robot successfully navigating all validation tests. When trained with other approaches, the robot only succeeds in forward locomotion test and fails when turning is required.

Related papers

• Gait in Eight: Efficient On-Robot Learning for Omnidirectional Quadruped Locomotion [13.314871831095882]
  On-robot Reinforcement Learning is a promising approach to train embodiment-aware policies for legged robots.<n>We present a framework for efficiently learning quadruped locomotion in just 8 minutes of raw real-time training.<n>We demonstrate the robustness of our approach in different indoor and outdoor environments.
  arXiv  Detail & Related papers  (2025-03-11T12:32:06Z)
• Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control [106.32794844077534]
  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)
• 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)
• GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots [87.32145104894754]
  We introduce a framework for training generalized locomotion (GenLoco) controllers for quadrupedal robots. Our framework synthesizes general-purpose locomotion controllers that can be deployed on a large variety of quadrupedal robots. We show that our models acquire more general control strategies that can be directly transferred to novel simulated and real-world robots.
  arXiv  Detail & Related papers  (2022-09-12T15:14:32Z)
• REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer [57.045140028275036]
  We consider the problem of transferring a policy across two different robots with significantly different parameters such as kinematics and morphology. Existing approaches that train a new policy by matching the action or state transition distribution, including imitation learning methods, fail due to optimal action and/or state distribution being mismatched in different robots. We propose a novel method named $REvolveR$ of using continuous evolutionary models for robotic policy transfer implemented in a physics simulator.
  arXiv  Detail & Related papers  (2022-02-10T18:50:25Z)
• Adaptation of Quadruped Robot Locomotion with Meta-Learning [64.71260357476602]
  We demonstrate that meta-reinforcement learning can be used to successfully train a robot capable to solve a wide range of locomotion tasks. The performance of the meta-trained robot is similar to that of a robot that is trained on a single task.
  arXiv  Detail & Related papers  (2021-07-08T10:37:18Z)
• Learning Bipedal Robot Locomotion from Human Movement [0.791553652441325]
  We present a reinforcement learning based method for teaching a real world bipedal robot to perform movements directly from motion capture data. Our method seamlessly transitions from training in a simulation environment to executing on a physical robot. We demonstrate our method on an internally developed humanoid robot with movements ranging from a dynamic walk cycle to complex balancing and waving.
  arXiv  Detail & Related papers  (2021-05-26T00:49:37Z)
• Learning Agile Locomotion via Adversarial Training [59.03007947334165]
  In this paper, we present a multi-agent learning system, in which a quadruped robot (protagonist) learns to chase another robot (adversary) while the latter learns to escape. We find that this adversarial training process not only encourages agile behaviors but also effectively alleviates the laborious environment design effort. In contrast to prior works that used only one adversary, we find that training an ensemble of adversaries, each of which specializes in a different escaping strategy, is essential for the protagonist to master agility.
  arXiv  Detail & Related papers  (2020-08-03T01:20:37Z)
• Learning Stable Manoeuvres in Quadruped Robots from Expert Demonstrations [3.893720742556156]
  Key problem is to generate leg trajectories for continuously varying target linear and angular velocities. We propose a two pronged approach to address this problem. We develop a neural network-based filter that takes in target velocity, radius and transforms them into new commands.
  arXiv  Detail & Related papers  (2020-07-28T15:02:04Z)
• 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.