Legged robots that balance
Omnidirectional Locomotion for Quadruped Robots
RoboCup 2001: Robot Soccer World Cup V
Convex Optimization
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Modeling and Experiments of Untethered Quadrupedal Running with a Bounding Gait: The Scout II Robot
International Journal of Robotics Research
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Learning structured prediction models: a large margin approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Adaptive Dynamic Walking of a Quadruped Robot on Natural Ground Based on Biological Concepts
International Journal of Robotics Research
Self-organized adaptive legged locomotion in a compliant quadruped robot
Autonomous Robots
Machine learning for fast quadrupedal locomotion
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Stereo vision and terrain modeling for quadruped robots
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Autonomous evolution of dynamic gaits with two quadruped robots
IEEE Transactions on Robotics
Smooth Vertical Surface Climbing With Directional Adhesion
IEEE Transactions on Robotics
Improving traversability of quadruped walking robots using body movement in 3D rough terrains
Robotics and Autonomous Systems
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Legged robots have the potential to navigate a wide variety of terrain that is inaccessible to wheeled vehicles. In this paper we consider the planning and control tasks of navigating a quadruped robot over challenging terrain, including terrain that it has not seen until run-time. We present a software architecture that makes use of both static and dynamic gaits, as well as specialized dynamic maneuvers, to accomplish this task. Throughout the paper we highlight two themes that have been central to our approach: (1) the prevalent use of learning algorithms, and (2) a focus on rapid recovery and replanning techniques; we present several novel methods and algorithms that we developed for the quadruped and that illustrate these two themes. We evaluate the performance of these different methods, and also present and discuss the performance of our system on the official Learning Locomotion tests.