Unsupervised Learning of Effective Actions in Robotics
Marko Zaric Jakob Hollenstein Justus Piater Erwan Renaudo
Learning actions that are relevant to decision-making and can be executed
effectively is a key problem in autonomous robotics. Current state-of-the-art
action representations in robotics lack proper effect-driven learning of the
robot's actions. Although successful in solving manipulation tasks, deep
learning methods also lack this ability, in addition to their high cost in
terms of memory or training data. In this paper, we propose an unsupervised
algorithm to discretize a continuous motion space and generate "action
prototypes", each producing different effects in the environment. After an
exploration phase, the algorithm automatically builds a representation of the
effects and groups motions into action prototypes, where motions more likely to
produce an effect are represented more than those that lead to negligible
changes. We evaluate our method on a simulated stair-climbing reinforcement
learning task, and the preliminary results show that our effect driven
discretization outperforms uniformly and randomly sampled discretizations in
convergence speed and maximum reward.