Publications
Below is a list of all relevant publications authored by Robotics Forum members.
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Journal articleShen H, Yosinski J, Kormushev P, et al., 2012,
Learning Fast Quadruped Robot Gaits with the RL PoWER Spline Parameterization
, International Journal of Cybernetics and Information Technologies, Vol: 12 -
Journal articleDallali H, Kormushev P, Li Z, et al., 2012,
On Global Optimization of Walking Gaits for the Compliant Humanoid Robot COMAN Using Reinforcement Learning
, International Journal of Cybernetics and Information Technologies, Vol: 12 -
Conference paperLane DM, Maurelli F, Kormushev P, et al., 2012,
Persistent Autonomy: the Challenges of the PANDORA Project
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Journal articleCarrera A, Ahmadzadeh SR, Ajoudani A, et al., 2012,
Towards Autonomous Robotic Valve Turning
, Cybernetics and Information Technologies, Vol: 12, Pages: 17-26 -
Conference paperKormushev P, Calinon S, Ugurlu B, et al., 2012,
Challenges for the policy representation when applying reinforcement learning in robotics
, Pages: 1-8 -
Conference paperKormushev P, Ugurlu B, Colasanto L, et al., 2012,
The anatomy of a fall: Automated real-time analysis of raw force sensor data from bipedal walking robots and humans
, Pages: 3706-3713 -
Journal articleCalinon S, Kormushev P, Caldwell DG, 2012,
Compliant skills acquisition and multi-optima policy search with EM-based reinforcement learning
, Robotics and Autonomous Systems -
Journal articleLeonetti M, Kormushev P, Sagratella S, 2012,
Combining Local and Global Direct Derivative-free Optimization for Reinforcement Learning
, International Journal of Cybernetics and Information Technologies, Vol: 12 -
Conference paperKormushev P, Ugurlu B, Calinon S, et al., 2011,
Bipedal Walking Energy Minimization by Reinforcement Learning with Evolving Policy Parameterization
, Pages: 318-324 -
Conference paperKormushev P, Ugurlu B, Calinon S, et al., 2011,
Bipedal walking energy minimization by reinforcement learning with evolving policy parameterization
, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), Publisher: IEEE -
Conference paperKormushev P, Nenchev DN, Calinon S, et al., 2011,
Upper-body Kinesthetic Teaching of a Free-standing Humanoid Robot
, Pages: 3970-3975 -
Journal articleKormushev P, Calinon S, Caldwell DG, 2011,
Imitation Learning of Positional and Force Skills Demonstrated via Kinesthetic Teaching and Haptic Input
, Advanced Robotics, Vol: 25, Pages: 581-603 -
Journal articleKormushev P, Nomoto K, Dong F, et al., 2011,
Time Hopping Technique for Faster Reinforcement Learning in Simulations
, International Journal of Cybernetics and Information Technologies, Vol: 11, Pages: 42-59 -
Conference paperKormushev P, Calinon S, Saegusa R, et al., 2010,
Learning the skill of archery by a humanoid robot iCub
, Pages: 417-423 -
Conference paperKormushev P, Calinon S, Caldwell DG, 2010,
Approaches for Learning Human-like Motor Skills which Require Variable Stiffness During Execution
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Conference paperKormushev P, Calinon S, Caldwell DG, 2010,
Robot Motor Skill Coordination with EM-based Reinforcement Learning
, Pages: 3232-3237 -
Conference paperSato F, Nishii T, Takahashi J, et al., 2010,
Whiteboard Cleaning Task Realization with HOAP-2
, Pages: 426-429 -
Conference paperKormushev P, Dong F, Hirota K, 2009,
Probability redistribution using time hopping for reinforcement learning
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Journal articleKormushev P, Nomoto K, Dong F, et al., 2009,
Eligibility Propagation to Speed up Time Hopping for Reinforcement Learning
, Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol: 13, No. 6 -
Journal articleKormushev P, Nomoto K, Dong F, et al., 2008,
Time manipulation technique for speeding up reinforcement learning in simulations
, Cybernetics and Information Technologies, Vol: 8, Pages: 12-24, ISSN: 1311-9702A technique for speeding up reinforcement learning algorithms by usingtime manipulation is proposed. It is applicable to failure-avoidance controlproblems running in a computer simulation. Turning the time of the simulationbackwards on failure events is shown to speed up the learning by 260% andimprove the state space exploration by 12% on the cart-pole balancing task,compared to the conventional Q-learning and Actor-Critic algorithms.
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