Event Location: NSH 1305

Abstract: Mobile robots are increasingly being deployed in the real world in response to a heightened demand for applications such as transportation, delivery and inspection. The motion planning systems for these robots are expected to have consistent performance across the wide range of scenarios that they encounter. While state-of-the art planners can be adapted to solve these real-time kinodynamic planning problems, their performance varies vastly across diverse scenarios. This thesis proposes that the motion planner for a mobile robot must adapt its search strategy to the distribution over planning problems that the robot encounters.

We address three principal challenges of this problem. Firstly, we show that even when the planning problem distribution is fixed, designing a non-adaptive planner can be challenging due to the unpredictability of its performance. We discuss how to alleviate this issue by leveraging a diverse ensemble of planners. Secondly, when the distribution is varying, we require a meta-planner that can use context to automatically select an ensemble from a library of black-box planners. We show both theoretically and empirically that greedily training a list of predictors to focus on failure cases leads to an effective meta-planner. Finally, in the interest of computational efficiency, we want a white-box planner that adapts its search strategy during a planning cycle. We show how such a strategy can be trained efficiently in a data-driven imitation learning framework.

Based on our preliminary investigations, we propose to examine three sub-problems that will lead to an effective adaptive motion planning framework. The first is learning heuristic and collision checking policies that optimize search effort by adapting to the distribution of obstacles in the environment. The second is to train context efficient meta-planners that use planner performance as additional feedback. The third is to automatically deal with failure cases that occur during online execution.

We evaluate the efficacy of our framework on a spectrum of motion planning problems with a primary focus on an autonomous full-scale helicopter. We expect that our framework will enable mobile robots to navigate seamlessly across different missions without the need for human intervention.

Committee:Sebastian Scherer, Chair

Siddhartha Srinivasa

Martial Hebert

Ashish Kapoor, Microsoft Research