Quadruped Robot Simulation Using Deep Reinforcement Learning -- A step towards locomotion policy
On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation
Abstract (arXiv)
We present a novel reinforcement learning method to train the quadruped robot in a simulated environment. The idea of controlling quadruped robots in a dynamic environment is quite challenging and my method presents the optimum policy and training scheme with limited resources and shows considerable performance. The report uses the raisimGymTorch open-source library and proprietary software RaiSim for the simulation of ANYmal robot. My approach is centered on formulating Markov decision processes using the evaluation of the robot walking scheme while training. Resulting MDPs are solved using a proximal policy optimization algorithm used in actor-critic mode and collected thousands of state transitions with a single desktop machine. This work also presents a controller scheme trained over thousands of time steps shown in a simulated environment. This work also sets the base for early-stage researchers to deploy their favorite algorithms and configurations. Keywords: Legged robots, deep reinforcement learning, quadruped robot simulation, optimal control
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 14 references that are in the map. First posted on arXiv 23 Feb 2025.
Builds on
4 of 14 references.
References · 4
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2019Learning agile and dynamic motor skills for legged robotscited by 504
- 2021Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robotscited by 116
- 2016An Efficient Optimal Planning and Control Framework For Quadrupedal Locomotioncited by 12
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- 2025Training Directional Locomotion for Quadrupedal Low-Cost Robotic Systems via Deep Reinforcement Learning
- 2024MASQ: Multi-Agent Reinforcement Learning for Single Quadruped Robot Locomotion
- 2025Acrobotics: A Generalist Approach to Quadrupedal Robots' Parkour
- 2025Sampling Strategies for Robust Universal Quadrupedal Locomotion Policies
- 2025Efficient Learning of A Unified Policy For Whole-body Manipulation and Locomotion Skills
- 2024Reinforcement Learning For Quadrupedal Locomotion: Current Advancements And Future Perspectives
- 2024Learning Quadruped Locomotion Using Differentiable Simulation
- 2023A Real-World Quadrupedal Locomotion Benchmark for Offline Reinforcement Learning