A major challenge for traditional approaches to multiagent learning is to train teams that easily scale to include additional agents. The problem is that such approaches typically...
David B. D'Ambrosio, Joel Lehman, Sebastian Risi, ...
Abstract— Learning techniques in robotic grasping applications have usually been concerned with the way a hand approaches to an object, or with improving the motor control of man...
Antonio Morales, Eris Chinellato, Andrew H. Fagg, ...
We plan to evaluate different kinds of augmented feedback (tactile, video, sound) for learning basic dance choreographies. Therefore we develop a dance training system based on mo...
— Assisting humans in their daily lives requires robots to be proficient in manual tasks and effective in communicating states/intentions with human users. This paper advocates ...
We present an expressive agent design language for reinforcement learning that allows the user to constrain the policies considered by the learning process.The language includes s...