Training and Simulation of Quadrupedal Robot in Adaptive Stair Climbing and Descending for Indoor Firefighting: An End-to-End Reinforcement Learning Approach
Independent Researcher（独立研究者）；Department of Construction Management, Western New England University（西雅图新英格兰大学建设管理系）
Baixiao Huang, Baiyu Huang, Yu Hou
备注：
8 pages, 9 figures, 43rd International Symposium on Automation and Robotics in Construction
Quadruped robots are used for primary searches during the early stages of indoor fires. A typical primary search involves quickly and thoroughly looking for victims under hazardous conditions and monitoring flammable materials. However, situational awareness in complex indoor environments and rapid stair climbing and descending across different staircases remain the main challenges for robot-assisted primary searches. In this project, we designed a two-stage end-to-end deep reinforcement learning (RL) approach to optimize both navigation and locomotion. In the first stage, the quadrupeds, Unitree Go2, were trained to climb and descend stairs in Isaac Lab's pyramid-stair terrain. In the second stage, the quadrupeds were trained to climb and descend various realistic indoor staircases in the Isaac Lab engine, with the learned policy transferred from the previous stage. These indoor staircases are straight, L-shaped, and spiral, to support climbing and descending tasks in complex environments. This project explores how to balance navigation and locomotion and how end-to-end RL methods can enable quadrupeds to adapt to different stair shapes. Our main contributions are: (1) A two-stage end-to-end RL framework that transfers climbing/descending skills from abstract pyramid terrain to realistic indoor stair topologies. (2) A centerline-based navigation formulation that enables unified learning of navigation and locomotion without hierarchical planning. (3) Demonstration of policy generalization across diverse staircases using only local height-map perception. (4) An empirical analysis of success, efficiency, and failure modes under increasing stair difficulty.
Shanghai Jiao Tong University（上海交通大学）；Digital Brain Lab（数字大脑实验室）；ShanghaiTech University（上海科技大学）；University College London（伦敦大学学院）
Deep reinforcement learning has recently emerged as an appealing alternative for legged locomotion over multiple terrains by training a policy in physical simulation and then transferring it to the real world (i.e., sim-to-real transfer). Despite considerable progress, the capacity and scalability of traditional neural networks are still limited, which may hinder their applications in more complex environments. In contrast, the Transformer architecture has shown its superiority in a wide range of large-scale sequence modeling tasks, including natural language processing and decision-making pro…
DreamWaQ++: Obstacle-Aware Quadrupedal Locomotion With Resilient Multi-Modal Reinforcement Learning
Urban Robotics Lab., School of Electrical Engineering, KAIST（韩国科学技术院电子工程系城市机器人实验室）；KRAFTON（KRAFTON公司）；URobotics（URobotics公司）；Laboratory for Information and Decision Systems (LIDS), MIT（麻省理工学院信息与决策系统实验室）
Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS)（深圳市人工智能与机器人研究院）；Institute of Robotics and Intelligent Manufacturing (IRIM), The Chinese University of Hong Kong (CUHK)（香港中文大学机器人与智能制造研究院）；George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology（佐治亚理工学院乔治·W·伍德拉夫机械工程学院）；Beijing Institute for General Artificial Intelligence（北京通用人工智能研究院）
Dynamic quadrupedal locomotion over rough terrains reveals remarkable progress over the last few decades. Small-scale quadruped robots are adequately flexible and adaptable to traverse uneven terrains along sagittal direction, such as slopes and stairs. To accomplish autonomous locomotion navigation in complex environments, spinning is a fundamental yet indispensable functionality for legged robots. However, spinning behaviors of quadruped robots on uneven terrain often exhibit position drifts. Motivated by this problem, this study presents an algorithmic method to enable accurate spinning mot…
Visvesvaraya National Institute of Technology（维斯瓦瓦拉亚国家理工学院）；NASA-JPL, Caltech（加州理工学院NASA喷气推进实验室）
The selection of mobility modes for robot navigation consists of various trade-offs. Snake robots are ideal for traversing through constrained environments such as pipes, cluttered and rough terrain, whereas bipedal robots are more suited for structured environments such as stairs. Finally, quadruped robots are more stable than bipeds and can carry larger payloads than snakes and bipeds but struggle to navigate soft soil, sand, ice, and constrained environments. A reconfigurable robot can achieve the best of all worlds. Unfortunately, state-of-the-art reconfigurable robots rely on the rearrang…
Terrain-Aware Quadrupedal Locomotion via Reinforcement Learning
Tencent Robotics X（腾讯Robotics X）；The Chinese University of Hong Kong（香港中文大学）；Southern University of Science and Technology（南方科技大学）；University of Alberta（阿尔伯塔大学）
Entropy-Controlled Intrinsic Motivation Reinforcement Learning for Quadruped Robot Locomotion in Complex Terrains
Faculty of Information Technology, Beijing University of Technology（信息技术学院，北京理工大学）；Faculty Nuclear Industry X Intelligence Laboratory, Beijing University of Technology（核工业X智能实验室，北京理工大学）；Department of Computer Science, University of Bristol（计算机科学系，布里斯托大学）
Evaluation of an Actuated Spine in Agile Quadruped Locomotion
Department of Computer Science, Technical University of Darmstadt（德意志技术大学达姆施塔特分校计算机科学系）；IDEAS Research Institute（IDEAS研究所）；Institute of Robotics and Machine Intelligence, Poznan University of Technology（波兹南技术大学机器人与机器智能研究所）
How can a robot navigate successfully in rich and diverse environments, indoors or outdoors, along office corridors or trails on the grassland, on the flat ground or the staircase? To this end, this work aims to address three challenges: (i) complex visual observations, (ii) partial observability of local visual sensing, and (iii) multimodal robot behaviors conditioned on both the local environment and the global navigation objective. We propose to train a neural network (NN) controller for local navigation via imitation learning. To tackle complex visual observations, we extract multi-scale s…
