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IJRR
2011
126views more  IJRR 2011»
13 years 6 months ago
Optimization and learning for rough terrain legged locomotion
We present a novel approach to legged locomotion over rough terrain that is thoroughly rooted in optimization. This approach relies on a hierarchy of fast, anytime algorithms to p...
Matthew Zucker, Nathan D. Ratliff, Martin Stolle, ...
CORR
2011
Springer
230views Education» more  CORR 2011»
13 years 6 months ago
Computational Rationalization: The Inverse Equilibrium Problem
Modeling the behavior of imperfect agents from a small number of observations is a difficult, but important task. In the singleagent decision-theoretic setting, inverse optimal co...
Kevin Waugh, Brian Ziebart, J. Andrew Bagnell
CORR
2011
Springer
188views Education» more  CORR 2011»
13 years 6 months ago
Information-Theoretic Viewpoints on Optimal Causal Coding-Decoding Problems
—In this paper we consider an interacting two-agent sequential decision-making problem consisting of a Markov source process, a causal encoder with feedback, and a causal decoder...
Siva K. Gorantla, Todd P. Coleman
CDC
2010
IEEE
137views Control Systems» more  CDC 2010»
13 years 6 months ago
Analysis of optimal control models for the human locomotion
In recent papers it has been suggested that human locomotion may be modeled as an inverse optimal control problem. In this paradigm, the trajectories are assumed to be solutions of...
Yacine Chitour, Francesca C. Chittaro, Fréd...
ICRA
2010
IEEE
115views Robotics» more  ICRA 2010»
13 years 10 months ago
An optimization approach to rough terrain locomotion
— We present a novel approach to legged locomotion over rough terrain that is thoroughly rooted in optimization. This approach relies on a hierarchy of fast, anytime algorithms t...
Matthew Zucker, James A. Bagnell, Christopher G. A...
ICML
2010
IEEE
14 years 15 days ago
Inverse Optimal Control with Linearly-Solvable MDPs
We present new algorithms for inverse optimal control (or inverse reinforcement learning, IRL) within the framework of linearlysolvable MDPs (LMDPs). Unlike most prior IRL algorit...
Dvijotham Krishnamurthy, Emanuel Todorov