ArticleBiomimetics (Basel, Switzerland)2023
A Hierarchical Framework for Quadruped Robots Gait Planning Based on DDPG.
Article in Biomimetics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Deep self-attention reinforcement learning adaptive gait planning and control for lower limb rehabilitation exoskeletons robot.iScience · 2026Article
- AI-driven quadruped robots: from fundamental locomotion to advanced biomimetic behaviors.Frontiers in neurorobotics · 2026Review
- Biped Robots Control in Gusty Environments with Adaptive Exploration Based DDPG.Biomimetics (Basel, Switzerland) · 2024Article
- Trajectory Tracking Control of Variable Sweep Aircraft Based on Reinforcement Learning.Biomimetics (Basel, Switzerland) · 2024Article
- Special Issue: Design and Control of a Bio-Inspired Robot.Biomimetics (Basel, Switzerland) · 2024Article
- A Hierarchical Control Strategy for a Rigid-Flexible Coupled Hexapod Bio-Robot.Biomimetics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In recent years, significant progress has been made in employing reinforcement learning for controlling legged robots. However, a major challenge arises with quadruped robots due to their continuous states and vast action space, making optimal control using simple reinforcement learning controllers particularly challenging. This paper introduces a hierarchical reinforcement learning framework based on the Deep Deterministic Policy Gradient (DDPG) algorithm to achieve optimal motion control for quadruped robots. The framework consists of a high-level planner responsible for generating ideal motion parameters, a low-level controller using model predictive control (MPC), and a trajectory generator. The agents within the high-level planner are trained to provide the ideal motion parameters for the low-level controller. The low-level controller uses MPC and PD controllers to generate the foot-end force and calculates the joint motor torque through inverse kinematics. The simulation results show that the motion performance of the trained hierarchical framework is superior to that obtained using only the DDPG method.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.