5 accepted papers
In order to provide adaptive and user-friendly solutions to robotic manipulation; it is important that the agent can learn to accomplish tasks even if they are only provided with very sparse instruction signals. To address the issues reinforcement learning algorithms face when task rewards are spars…
This paper presents a hierarchical motion planning approach that can provide real-time parking plans for autonomous vehicles with limited memory. Through combining a high-level route planner that searches for collision-free routes given traffic and obstacle information and a low level motion planner…
This paper introduces Probabilistic Chekov (p-Chekov), a chance-constrained motion planning system that can be applied to high degree-of-freedom (DOF) robots under motion uncertainty and imperfect state information. Given process and observation noise models, it can find feasible trajectories which…
Deployment of motion planning algorithms in practical applications has lagged due to their slow speed in reacting to disturbances. We believe that the best way to address this is to reuse learned planning and control information across queries. In previous work, we introduced Chekov, a reactive, int…
We present an evaluation of several representative sampling-based and optimization-based motion planners, and then introduce an integrated motion planning system which incorporates recent advances in trajectory optimization into a sparse roadmap framework. Through experiments in 4 common application…