Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots
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Abstract (arXiv)
Developing robust walking controllers for bipedal robots is a challenging endeavor. Traditional model-based locomotion controllers require simplifying assumptions and careful modelling; any small errors can result in unstable control. To address these challenges for bipedal locomotion, we present a model-free reinforcement learning framework for training robust locomotion policies in simulation, which can then be transferred to a real bipedal Cassie robot. To facilitate sim-to-real transfer, domain randomization is used to encourage the policies to learn behaviors that are robust across variations in system dynamics. The learned policies enable Cassie to perform a set of diverse and dynamic behaviors, while also being more robust than traditional controllers and prior learning-based methods that use residual control. We demonstrate this on versatile walking behaviors such as tracking a target walking velocity, walking height, and turning yaw.
Key facts. Cited by 95 papers in the map (data through 7 Oct 2026). Builds on 20 of its 39 references that are in the map. First posted on arXiv 26 Mar 2021.
Builds on
20 of 39 references.
References · 20
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
- 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 616
- 2020Learning Quadrupedal Locomotion over Challenging Terraincited by 494
- 2019Learning agile and dynamic motor skills for legged robotscited by 402
- 2018Residual Reinforcement Learning for Robot Controlcited by 242
- 2020Learning Agile Robotic Locomotion Skills by Imitating Animalscited by 229
- 2016CAD2RL: Real Single-Image Flight without a Single Real Imagecited by 227
- 2018DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skillscited by 153
- 2019Sim-to-Real Transfer for Biped Locomotioncited by 50
- 2019Learning Fast Adaptation with Meta Strategy Optimizationcited by 46
- 2018Feedback Control For Cassie With Deep Reinforcement Learningcited by 36
8 more
- 2020Learning Memory-Based Control for Human-Scale Bipedal Locomotioncited by 20
- 2018Feedback Control of a Cassie Bipedal Robot: Walking, Standing, and Riding a Segwaycited by 15
- 2019Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learningcited by 13
- 2020Animated Cassie: A Dynamic Relatable Robotic Charactercited by 12
- 2019Hybrid Zero Dynamics Inspired Feedback Control Policy Design for 3D Bipedal Locomotion using Reinforcement Learningcited by 7
- 2013An Efficiently Solvable Quadratic Program for Stabilizing Dynamic Locomotioncited by 6
- 2018Rapid Trajectory Optimization Using C-FROST with Illustration on a Cassie-Series Dynamic Walking Bipedcited by 6
- 2020Velocity Regulation of 3D Bipedal Walking Robots with Uncertain Dynamics Through Adaptive Neural Network Controllercited by 2
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Citing papers per year: 2021: 5; 2022: 20; 2023: 20; 2024: 29; 2025: 16; 2026: 5.
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