GainAdaptor: Learning Quadrupedal Locomotion with Dual Actors for Adaptable and Energy-Efficient Walking on Various Terrains

• URL: http://arxiv.org/abs/2412.09520v1
• Date: Thu, 12 Dec 2024 18:06:22 GMT
• Title: GainAdaptor: Learning Quadrupedal Locomotion with Dual Actors for Adaptable and Energy-Efficient Walking on Various Terrains
• Authors: Mincheol Kim, Nahyun Kwon, Jung-Yup Kim,
• Abstract summary: Deep reinforcement learning (DRL) has emerged as an innovative solution for controlling legged robots in challenging environments.<n>We propose GainAdaptor, an adaptive gain control framework that autonomously tunes joint PD gains to enhance terrain adaptability and energy efficiency.<n>We validate the effectiveness of the proposed method through experiments conducted on a Unitree Go1 robot, demonstrating improved locomotion performance across diverse terrains.
• Score: 0.8739101659113157
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep reinforcement learning (DRL) has emerged as an innovative solution for controlling legged robots in challenging environments using minimalist architectures. Traditional control methods for legged robots, such as inverse dynamics, either directly manage joint torques or use proportional-derivative (PD) controllers to regulate joint positions at a higher level. In case of DRL, direct torque control presents significant challenges, leading to a preference for joint position control. However, this approach necessitates careful adjustment of joint PD gains, which can limit both adaptability and efficiency. In this paper, we propose GainAdaptor, an adaptive gain control framework that autonomously tunes joint PD gains to enhance terrain adaptability and energy efficiency. The framework employs a dual-actor algorithm to dynamically adjust the PD gains based on varying ground conditions. By utilizing a divided action space, GainAdaptor efficiently learns stable and energy-efficient locomotion. We validate the effectiveness of the proposed method through experiments conducted on a Unitree Go1 robot, demonstrating improved locomotion performance across diverse terrains.

Related papers

• MULE: Multi-terrain and Unknown Load Adaptation for Effective Quadrupedal Locomotion [1.479858319622657]
  Quadrupedal robots are increasingly deployed for load-carrying tasks across diverse terrains. We propose an Adaptive Reinforcement Learning framework that enables quadrupedal robots to adapt to both varying payloads and diverse terrains. We validate the proposed approach through large-scale simulation experiments in Isaac Gym and real-world hardware deployment on a Unitree Go1 quadruped.
  arXiv  Detail & Related papers  (2025-05-01T12:41:35Z)
• Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves [69.9104427437916]
  Multi-generator Wave Energy Converters (WEC) must handle multiple simultaneous waves coming from different directions called spread waves. These complex devices need controllers with multiple objectives of energy capture efficiency, reduction of structural stress to limit maintenance, and proactive protection against high waves. In this paper, we explore different function approximations for the policy and critic networks in modeling the sequential nature of the system dynamics.
  arXiv  Detail & Related papers  (2024-04-17T02:04:10Z)
• 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)
• Combining model-predictive control and predictive reinforcement learning for stable quadrupedal robot locomotion [0.0]
  We study how this can be achieved by a combination of model-predictive and predictive reinforcement learning controllers. In this work, we combine both control methods to address the quadrupedal robot stable gate generation problem.
  arXiv  Detail & Related papers  (2023-07-15T09:22:37Z)
• Self-Tuning PID Control via a Hybrid Actor-Critic-Based Neural Structure for Quadcopter Control [0.0]
  Proportional-Integrator-Derivative (PID) controller is used in a wide range of industrial and experimental processes. Due to the uncertainty of model parameters and external disturbances, real systems such as Quadrotors need more robust and reliable PID controllers. In this research, a self-tuning PID controller using a Reinforcement-Learning-based Neural Network has been investigated.
  arXiv  Detail & Related papers  (2023-07-03T19:35:52Z)
• A Learning-based Adaptive Compliance Method for Symmetric Bi-manual Manipulation [13.061684545690882]
  Symmetric bi-manual manipulation is an essential skill in on-orbit operations. Traditional methods have viewed motion planning and compliant control as two separate modules. We propose a novel Learning-based Adaptive Compliance algorithm (LAC) that improves the efficiency and robustness of symmetric bi-manual manipulation.
  arXiv  Detail & Related papers  (2023-03-27T14:51:29Z)
• Learning to Exploit Elastic Actuators for Quadruped Locomotion [7.9585932082270014]
  Spring-based actuators in legged locomotion provide energy-efficiency and improved performance, but increase the difficulty of controller design. We propose to learn model-free controllers directly on the real robot. We evaluate the proposed approach on the DLR elastic quadruped bert.
  arXiv  Detail & Related papers  (2022-09-15T09:43:17Z)
• Skip Training for Multi-Agent Reinforcement Learning Controller for Industrial Wave Energy Converters [94.84709449845352]
  Recent Wave Energy Converters (WEC) are equipped with multiple legs and generators to maximize energy generation. Traditional controllers have shown limitations to capture complex wave patterns and the controllers must efficiently maximize the energy capture. This paper introduces a Multi-Agent Reinforcement Learning controller (MARL), which outperforms the traditionally used spring damper controller.
  arXiv  Detail & Related papers  (2022-09-13T00:20:31Z)
• Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with Transformer [128.19212716007794]
  We propose a Transformer-based Multi-Agent Actor-Critic framework (T-MAAC) to stabilize voltage in power distribution networks. In addition, we adopt a novel auxiliary-task training process tailored to the voltage control task, which improves the sample efficiency.
  arXiv  Detail & Related papers  (2022-06-08T07:48:42Z)
• Efficient Learning of Control Policies for Robust Quadruped Bounding using Pretrained Neural Networks [15.09037992110481]
  Bounding is one of the important gaits in quadrupedal locomotion for negotiating obstacles. The authors proposed an effective approach that can learn robust bounding gaits more efficiently. The authors approach shows efficient computing and good locomotion results by the Jueying Mini quadrupedal robot bounding over uneven terrain.
  arXiv  Detail & Related papers  (2020-11-01T08:06:46Z)
• Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion [95.1825179206694]
  We present a framework that synthesizes robust controllers for a quadruped robot. A high-level controller learns to choose from a set of primitives in response to changes in the environment. A low-level controller that utilizes an established control method to robustly execute the primitives.
  arXiv  Detail & Related papers  (2020-09-21T16:49:26Z)
• Transfer Deep Reinforcement Learning-enabled Energy Management Strategy for Hybrid Tracked Vehicle [8.327437591702163]
  This paper proposes an adaptive energy management strategy for hybrid electric vehicles by combining deep reinforcement learning (DRL) and transfer learning (TL) It aims to address the defect of DRL in tedious training time. The founded DRL and TL-enabled control policy is capable of enhancing energy efficiency and improving system performance.
  arXiv  Detail & Related papers  (2020-07-16T23:39:34Z)
• Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications [54.610318402371185]
  Intelligent reflecting surface (IRS) is a promising technology to assist downlink information transmissions from a multi-antenna access point (AP) to a receiver. We minimize the AP's transmit power by a joint optimization of the AP's active beamforming and the IRS's passive beamforming. We propose a deep reinforcement learning (DRL) approach that can adapt the beamforming strategies from past experiences.
  arXiv  Detail & Related papers  (2020-05-25T01:42:55Z)

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.