International Journal of Robotics Research
Analysis of a simplified hopping robot
International Journal of Robotics Research
Barycentric interpolators for continuous space & time reinforcement learning
Proceedings of the 1998 conference on Advances in neural information processing systems II
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Intelligent Control for an Acrobot
Journal of Intelligent and Robotic Systems
Technical Update: Least-Squares Temporal Difference Learning
Machine Learning
Variable Resolution Discretization in Optimal Control
Machine Learning
Fastest Mixing Markov Chain on a Graph
SIAM Review
Applied optimal control for dynamically stable legged locomotion
Applied optimal control for dynamically stable legged locomotion
Incremental Online Learning in High Dimensions
Neural Computation
Minimalistic control of a compass gait robot in rough terrain
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Minimalistic control of biped walking in rough terrain
Autonomous Robots
Optimizing walking controllers for uncertain inputs and environments
ACM SIGGRAPH 2010 papers
Fully interconnected, linear control for limit cycle walking
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Stable dynamic walking over uneven terrain
International Journal of Robotics Research
Quantifying disturbance rejection of SLIP-like running systems
International Journal of Robotics Research
Finite-time regional verification of stochastic non-linear systems
International Journal of Robotics Research
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Linear reactive control for efficient 2D and 3D bipedal walking over rough terrain
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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Legged robots that operate in the real world are inherently subject to stochasticity in their dynamics and uncertainty about the terrain. Owing to limited energy budgets and limited control authority, these 芒聙聹disturbances芒聙聺 cannot always be canceled out with high-gain feedback. Minimally actuated walking machines subject to stochastic disturbances no longer satisfy strict conditions for limit-cycle stability; however, they can still demonstrate impressively long-living periods of continuous walking. Here, we employ tools from stochastic processes to examine the 芒聙聹stochastic stability芒聙聺 of idealized rimless-wheel and compass-gait walking on randomly generated uneven terrain. Furthermore, we employ tools from numerical stochastic optimal control to design a controller for an actuated compass gait model which maximizes a measure of stochastic stability芒聙聰the mean first-passage time芒聙聰and compare its performance with a deterministic counterpart. Our results demonstrate that walking is well characterized as a metastable process, and that the stochastic dynamics of walking should be accounted for during control design in order to improve the stability of our machines.