Learning Free Gait Transition for Quadruped Robots via Phase-Guided Controller

• URL: http://arxiv.org/abs/2201.00206v1
• Date: Sat, 1 Jan 2022 15:15:42 GMT
• Title: Learning Free Gait Transition for Quadruped Robots via Phase-Guided Controller
• Authors: Yecheng Shao, Yongbin Jin, Xianwei Liu, Weiyan He, Hongtao Wang, Wei Yang
• Abstract summary: We present a novel framework for training a simple control policy for a quadruped robot to locomote in various gaits. The Black Panther robot, a medium-dog-sized quadruped robot, can perform all learned motor skills while following the velocity commands smoothly and robustly in natural environment.
• Score: 4.110347671351065
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Gaits and transitions are key components in legged locomotion. For legged robots, describing and reproducing gaits as well as transitions remain longstanding challenges. Reinforcement learning has become a powerful tool to formulate controllers for legged robots. Learning multiple gaits and transitions, nevertheless, is related to the multi-task learning problems. In this work, we present a novel framework for training a simple control policy for a quadruped robot to locomote in various gaits. Four independent phases are used as the interface between the gait generator and the control policy, which characterizes the movement of four feet. Guided by the phases, the quadruped robot is able to locomote according to the generated gaits, such as walk, trot, pacing and bounding, and to make transitions among those gaits. More general phases can be used to generate complex gaits, such as mixed rhythmic dancing. With the control policy, the Black Panther robot, a medium-dog-sized quadruped robot, can perform all learned motor skills while following the velocity commands smoothly and robustly in natural environment.

Related papers

• Learning Visual Quadrupedal Loco-Manipulation from Demonstrations [36.1894630015056]
  We aim to empower a quadruped robot to execute real-world manipulation tasks using only its legs. We decompose the loco-manipulation process into a low-level reinforcement learning (RL)-based controller and a high-level Behavior Cloning (BC)-based planner. Our approach is validated through simulations and real-world experiments, demonstrating the robot's ability to perform tasks that demand mobility and high precision.
  arXiv  Detail & Related papers  (2024-03-29T17:59:05Z)
• 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)
• Barkour: Benchmarking Animal-level Agility with Quadruped Robots [70.97471756305463]
  We introduce the Barkour benchmark, an obstacle course to quantify agility for legged robots. Inspired by dog agility competitions, it consists of diverse obstacles and a time based scoring mechanism. We present two methods for tackling the benchmark.
  arXiv  Detail & Related papers  (2023-05-24T02:49:43Z)
• 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)
• Learning Quadruped Locomotion Policies using Logical Rules [2.008081703108095]
  We aim to enable easy gait specification and efficient policy learning for quadruped robots. Our approach is called RM-based Locomotion Learning(RMLL), and supports adjusting gait frequency at execution time. We demonstrate these learned policies with a real quadruped robot.
  arXiv  Detail & Related papers  (2021-07-23T00:37:32Z)
• 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)
• Fast and Efficient Locomotion via Learned Gait Transitions [35.86279693549959]
  We focus on the problem of developing efficient controllers for quadrupedal robots. We devise a hierarchical learning framework, in which distinctive locomotion gaits and natural gait transitions emerge automatically. We show that the learned hierarchical controller consumes much less energy across a wide range of locomotion speed than baseline controllers.
  arXiv  Detail & Related papers  (2021-04-09T23:53:28Z)
• Learning Quadrupedal Locomotion over Challenging Terrain [68.51539602703662]
  Legged locomotion can dramatically expand the operational domains of robotics. Conventional controllers for legged locomotion are based on elaborate state machines that explicitly trigger the execution of motion primitives and reflexes. Here we present a radically robust controller for legged locomotion in challenging natural environments.
  arXiv  Detail & Related papers  (2020-10-21T19:11:20Z)
• 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.