Deep reinforcement learning (DRL) has emerged as a promising solution to mastering explosive and versatile quadrupedal jumping skills. However, current DRL-based frameworks usually rely on pre-existing reference trajectories obtained by capturing animal motions or transferring experience from existing controllers. This work aims to prove that learning dynamic jumping is possible without relying on imitating a reference trajectory by leveraging a curriculum design. Starting from a vertical in-place jump, we generalize the learned policy to forward and diagonal jumps and, finally, we learn to jump across obstacles. Conditioned on the desired landing location, orientation, and obstacle dimensions, the proposed approach yields a wide range of omnidirectional jumping motions in real-world experiments. Particularly we achieve a 90cm forward jump, exceeding all previous records for similar robots reported in the existing literature. Additionally, the robot can reliably execute continuous jumping on soft grassy grounds, which is especially remarkable as such conditions were not included in the training stage.
A supplementary video can be found on: https://www.youtube.com/watch?v=nRaMCrwU5X8. The code associated with this work can be found on: https://github.com/Vassil17/Curriculum-Quadruped-Jumping-DRL.
SF-TIM: A Simple Framework for Enhancing Quadrupedal Robot Jumping Agility by Combining Terrain Imagination and Measurement
State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, China（浙江大学现代光学仪器国家重点实验室）；School of Artificial Intelligence and Robotics and National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, China（湖南大学人工智能与机器人学院和机器人视觉感知与控制技术国家工程研究中心）；State Key Laboratory of Industrial Control Technology, Zhejiang University, China（浙江大学工业控制技术国家重点实验室）；DeepRobotics Co. Ltd., China（深睿科技有限公司）
SRL: Combining SLIP Model and Reinforcement Learning for Agile Robotic Jumping
Institute of Artificial Intelligence, Shanghai University（上海大学人工智能研究院）；Institute of Machine Intelligence, University of Shanghai for Science and Technology（上海理工大学机器智能研究院）
Versatile, Robust, and Explosive Locomotion with Rigid and Articulated Compliant Quadrupeds
Cognitive Robotics, Delft University of Technology（代尔夫特理工大学认知机器人学系）；University of Trento（特伦托大学）；University of Manchester（曼彻斯特大学）；Institute of Robotics and Mechatronics, German Aerospace Center (DLR)（德国航空航天中心机器人与机电一体化研究所）
EFGCL: Learning Dynamic Motion through Spotting-Inspired External Force Guided Curriculum Learning
Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo（机械信息学系，信息科学和技术研究生院，东京大学）；AI Center, Graduate School of Information Science and Technology, The University of Tokyo（人工智能中心，信息科学和技术研究生院，东京大学）
Reference-Free Sampling-Based Model Predictive Control
Inria - Département d’Informatique de l’École normale supérieure, PSL Research University（法国国家科学研究中心（Inria）- 工业信息学院，巴黎综合理工学院，PSL研究大学）；Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, ISIR（索邦大学，法国国家科学研究中心，智能系统与机器人研究所，ISIR）
