A Framework for Manipulation Planning and Execution under Uncertainty in Partially-Known Environments

There is a pressing need to make today’s robots capable, robust, and efficient during real-world operation. This proposal focuses on near-future scenarios that require complex long-horizon reasoning with non-trivial constraints on the robot’s motion. Examples include a robot operating in a warehouse with a partly automated storage and retrieval system or a robot running experiments in an automated laboratory. Several methods in robotics address the challenges of the above scenarios with explicit and carefully crafted planning domains that model how the robot interacts with the environment. Planning domains provide an abstraction over the world that is essential for Task and Motion Planning (TAMP) methods that plan over long horizons, that is, compute executable complex plans that require many steps or have non-monotonic properties, such as rearranging objects on shelves or fetching reactants for an experiment. This proposal will develop interpretable TAMP methods with the capability to deal with increasing uncertainty in the environment, while not sacrificing their strengths and providing a structured framework that allows for a meaningful connection with emerging model-free approaches. The project makes fundamental contributions to the core robotics problem of effective and efficient long-horizon planning under uncertainty by bringing together ideas from motion planning and control under uncertainty, optimization theory, probability and statistics, inverse reinforcement learning, imitation learning, high-dimensional search, factor graphs, probabilistic inference, classical AI planning, and formal methods.

This work has been supported by grant NSF CCF 2336612.

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