Robot Dynamics Algorithm
IROS '95 Proceedings of the International Conference on Intelligent Robots and Systems-Volume 1 - Volume 1
Adaptive Virtual Model Control of a Bipedal Walking Robot
INTSYS '98 Proceedings of the IEEE International Joint Symposia on Intelligence and Systems
A Reflexive Neural Network for Dynamic Biped Walking Control
Neural Computation
Rigid Body Dynamics Algorithms
Rigid Body Dynamics Algorithms
Asymptotically stable walking of a five-link underactuated 3-D bipedal robot
IEEE Transactions on Robotics
Integration of multi-level postural balancing on humanoid robots
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
The Yobotics-IHMC lower body humanoid robot
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Real time motion generation and control for biped robot: 1st report: walking gait pattern generation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Generalized biped walking control
ACM SIGGRAPH 2010 papers
A Compliant Hybrid Zero Dynamics Controller for Stable, Efficient and Fast Bipedal Walking on MABEL
International Journal of Robotics Research
International Journal of Robotics Research
International Journal of Robotics Research
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This two-part paper discusses the analysis and control of legged locomotion in terms of N-step capturability: the ability of a legged system to come to a stop without falling by taking N or fewer steps. We consider this ability to be crucial to legged locomotion and a useful, yet not overly restrictive criterion for stability. Part 1 introduced the N-step capturability framework and showed how to obtain capture regions and control sequences for simplified gait models. In Part 2, we describe an algorithm that uses these results as approximations to control a humanoid robot. The main contributions of this part are (1) step location adjustment using the 1-step capture region, (2) novel instantaneous capture point control strategies, and 3) an experimental evaluation of the 1-step capturability margin. The presented algorithm was tested using M2V2, a 3D force-controlled bipedal robot with 12 actuated degrees of freedom in the legs, both in simulation and in physical experiments. The physical robot was able to recover from forward and sideways pushes of up to 21 Ns while balancing on one leg and stepping to regain balance. The simulated robot was able to recover from sideways pushes of up to 15 Ns while walking, and walked across randomly placed stepping stones.