Robots with dynamics: efficient motion planning and analysis of controllers via machine learning
Creators: name: Sivaramakrishnan, Aravind
Publisher: name: No Publisher Supplied
Year: 2025

description: This thesis aims to improve the efficiency and robustness of motion planning for robots with significant dynamics, leveraging both advances in machine learning as well as contributions in algorithmic and foundational techniques. The key objectives are to (a) efficiently compute safe open-loop trajectories that obey non-trivial robot dynamics so that they are easy to follow with closed-loop controllers, and (b) efficiently analyze and characterize the capabilities of closed-loop robot controllers to enable safe real-world deployment.This effort starts by exploring alternatives to the standard methodology of generating control sequences in sampling-based planning for systems with dynamics. Typically, these methods rely on random controls, which are useful to argue desirable properties, but which lead to slow convergence and low-quality solutions in practice.To address this, the thesis first proposes using machine learning to train goal-reaching controllers via reinforcement learning. Such learned controllers can be integrated with sampling-based planners and help guide the expansion of the underlying planning structure towards the global goal. This is shown to lead to the faster discovery of high-quality trajectories on mobile robot navigation problems, including for physically-simulated challenges with uneven terrains.In addition, this thesis proposes the offline construction of a “roadmap with gaps” data structure for systems with dynamics, which can express the learned controller's reachability capabilities in a target environment. Online, the sampling-based planner uses the “roadmap with gaps” to promote the fact discovery of high-quality trajectories to the goal. The overall approach enhances the efficiency of motion planning in various benchmarks, including physics-based simulations of vehicular systems and aerial robots.The open-loop solutions generated by sampling-based planners require closed-loop feedback control for reliable real-world execution. To this end, the thesis first integrates techniques for identifying approximate analytical models of the robot's dynamics that allow fast motion planning and reduce the model gap. It then focuses on achieving closed-loop operation at both the planning and control levels by proposing a safe replanning framework for kinodynamic motion planning and integrating feedback controllers that reason about robot dynamics. These contributions allow for safe and efficient tracking of planned trajectories on a physical platform.Concurrently, the thesis also addresses the challenge of understanding the global dynamics of robot controllers, including learned ones, which is crucial for safe deployment of such solutions and the composition of controllers. A topological framework (Morse Graphs) is leveraged, and data-driven modeling approaches are proposed to enable data-efficient characterization of controller attractors and their regions of attraction, even for high-dimensional systems.Finally, the thesis contributes an open-source software library, which provides a flexible and efficient framework for integrating machine learning methods into kinodynamic planning and control.