Not Only Rewards But Also Constraints: Applications on Legged Robot Locomotion

IEEE Transactions on Robotics (T-RO) 2024

Yunho Kim    Hyunsik Oh    Jeonghyun Lee    Jinhyeok Choi
Gwanghyeon Ji    Moonkyu Jung    Donghoon Youm    Jemin Hwangbo

Korea Advanced Institute of Science and Technology (KAIST)


Several earlier studies have shown impressive control performance in complex robotic systems by designing the controller using a neural network and training it with model-free reinforcement learning. However, these outstanding controllers with natural motion style and high task performance are developed through extensive reward engineering, which is a highly laborious and time-consuming process of designing numerous reward terms and determining suitable reward coefficients. In this work, we propose a novel reinforcement learning framework for training neural network controllers for complex robotic systems consisting of both rewards and constraints. To let the engineers appropriately reflect their intent to constraints and handle them with minimal computation overhead, two constraint types and an efficient policy optimization algorithm are suggested. The learning framework is applied to train locomotion controllers for several legged robots with different morphology and physical attributes to traverse challenging terrains. Extensive simulation and real-world experiments demonstrate that performant controllers can be trained with significantly less reward engineering, by tuning only a single reward coefficient. Furthermore, a more straightforward and intuitive engineering process can be utilized, thanks to the interpretability and generalizability of constraints.

Paper: [link]       ArXiv preprint: [link]       Experiment video: [video 1] [video 2] [video 3]



    author={Kim, Yunho and Oh, Hyunsik and Lee, Jeonghyun and Choi, Jinhyeok and Ji, Gwanghyeon and Jung, Moonkyu and Youm, Donghoon and Hwangbo, Jemin},
    journal={IEEE Transactions on Robotics}, 
    title={Not Only Rewards but Also Constraints: Applications on Legged Robot Locomotion}, 
    keywords={Robots;Legged locomotion;Reinforcement learning;Optimization;Neural networks;Quadrupedal robots;Training;Constrained reinforcement learning (RL);legged locomotion;RL},