101 results found
Kormushev P, Dong F, Hirota K, 2009, Probability redistribution using time hopping for reinforcement learning
Kormushev P, 2009, Time Hopping Technique for Reinforcement Learning and its Application to Robot Control
Kormushev 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: 1311-9702
A 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.
Yamazaki Y, Dong F, Masuda Y, et al., 2007, Fuzzy inference based mentality estimation for eye robot agent
Yamazaki Y, Dong F, Masuda Y, et al., 2007, Intent expression using eye robot for mascot robot system
Agre G, Kormushev P, Dilov I, 2006, INFRAWEBS Axiom Editor - A graphical ontology-driven tool for creating complex logical expressions, International Journal of Information Theories and Applications, Vol: 13, Pages: 169-178
Kormushev P, 2006, Visual approach for data mining on medical information databases using Fastmap algorithm
Agre G, Kormushev P, Dilov I, 2006, INFRAWEBS Axiom Editor User’s Guide, Institute of Information Technologies, Bulgarian Academy of Sciences
Kormushev P, 2005, Design, development and implementation of a tool for construction of declarative functional descriptions of semantic web services based on WSMO methodology
Agre G, Kormushev P, Dilov I, 2005, INFRAWEBS Capability Editor - A graphical ontology-driven tool for creating capabilities of Semantic Web Services, Pages: 228-228
Chappell D, Wang K, Kormushev P, Asynchronous Real-Time Optimization of Footstep Placement and Timing in Bipedal Walking Robots
Online footstep planning is essential for bipedal walking robots to be ableto walk in the presence of disturbances. Until recently this has been achievedby only optimizing the placement of the footstep, keeping the duration of thestep constant. In this paper we introduce a footstep planner capable ofoptimizing footstep placement and timing in real-time by asynchronouslycombining two optimizers, which we refer to as asynchronous real-timeoptimization (ARTO). The first optimizer which runs at approximately 25 Hz,utilizes a fourth-order Runge-Kutta (RK4) method to accurately approximate thedynamics of the linear inverted pendulum (LIP) model for bipedal walking, thenuses non-linear optimization to find optimal footsteps and duration at a lowerfrequency. The second optimizer that runs at approximately 250 Hz, usesanalytical gradients derived from the full dynamics of the LIP model andconstraint penalty terms to perform gradient descent, which finds approximatelyoptimal footstep placement and timing at a higher frequency. By combining thetwo optimizers asynchronously, ARTO has the benefits of fast reactions todisturbances from the gradient descent optimizer, accurate solutions that avoidlocal optima from the RK4 optimizer, and increases the probability that afeasible solution will be found from the two optimizers. Experimentally, weshow that ARTO is able to recover from considerably larger pushes and producesfeasible solutions to larger reference velocity changes than a standardfootstep location optimizer, and outperforms using just the RK4 optimizeralone.
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