Domestic and real world robotics requires continuous learning of new skills and behaviors to interact with humans. Auto-supervised learning, a compromise between supervised and co...
We present a novel connectionist model for acquiring the semantics of a simple language through the behavioral experiences of a real robot. We focus on the “compositionality” ...
Reliable and efficient navigation with a humanoid robot is a difficult task. First, the motion commands are executed rather inaccurately due to backlash in the joints or foot slipp...
In many reinforcement learning applications, the set of possible actions can be partitioned by the programmer into subsets of similar actions. This paper presents a technique for ...
Continuous state spaces and stochastic, switching dynamics characterize a number of rich, realworld domains, such as robot navigation across varying terrain. We describe a reinfor...
Emma Brunskill, Bethany R. Leffler, Lihong Li, Mic...