Deep Reinforcement Learning Representations for Robotics
GHC 8102Abstract: A long standing goal of robotics research is to create algorithms that can automatically learn complex control strategies from scratch. Part of the challenge of applying such algorithms to robots is the choice of representation. While RL algorithms have been successfully applied to many robotics tasks such as Ball-in-a-Cup and various RoboCup soccer domains, [...]
Speeding Up Search-based Motion Planning Via Conservative Heuristics
GHC 6501Abstract: Weighted A* search (wA*) is a popular tool for robot motion-planning. Its efficiency however depends on the quality of heuristic function used. In fact, it has been shown that the correlation between the heuristic function and the true cost-to-goal significantly affects the efficiency of the search, when used with a large weight on the [...]
Learning multi-robot behaviors for online control
NSH 3305Abstract: Finding dynamically feasible and safe global plans for multi-agent teams in real world applications is enormously difficult because the decision branching factor, when considering all possible interactions across agents and an environment, is usually intractable. Humans, however, have great success in the multi-agent planning domain by using behaviors: practiced, coordinated responses for groups of [...]