6 accepted papers
In shared autonomy, the user input is blended with the assistive motion to accomplish a task where the user goal is typically unknown to the robot. Transparency between the human and robot is essential for effective collaboration. Prior works have provided methods for the robot to infer the user goa…
Performing safe and efficient lane changes is a crucial feature for creating fully autonomous vehicles. Recent advances have demonstrated successful lane following behavior using deep reinforcement learning, yet the interactions with other vehicles on-road for lane changes are rarely considered. In…
We explore the problem of learning to decompose spatial tasks into segments, as exemplified by the problem of a painting robot covering a large object. Inspired by the ability of classical decision tree algorithms to construct structured parti- tions of their input spaces, we formulate the problem o
Robots that navigate through human crowds need to be able to plan safe, efficient, and human predictable trajectories. This is a particularly challenging problem as it requires the robot to predict future human trajectories within a crowd where everyone implicitly cooperates with each other to avoid…
For robots to be a part of our daily life, they need to be able to navigate among crowds not only safely but also in a socially compliant fashion. This is a challenging problem because humans tend to navigate by implicitly cooperating with one another to avoid collisions, while heading toward their…
We formalize the problem of adapting a demonstrated trajectory to a new start and goal configuration as an optimization problem over a Hilbert space of trajectories: minimize the distance between the demonstration and the new trajectory subject to the new end point constraints. We show that the comm…