3 accepted papers
Deep reinforcement learning (RL) agents are able to learn contact-rich manipulation tasks by maximizing a reward signal, but require large amounts of experience, especially in environments with many obstacles that complicate exploration. In contrast, motion planners use explicit models of the agent
Planetary rovers, such as those currently on Mars, face difficult path planning problems, both before landing during the mission planning stages as well as once on the ground. In this work, we present a new approach to these planning problems based on inverse reinforcement learning using deep convol
Most current systems capable of robotic object manipulation involve ad hoc assumptions about the order of operations necessary to achieve a task, and usually have no mechanism to predict how earlier decisions will affect the quality of the solution later. Solving this problem is sometimes referred t…