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Dexterous Decision-Making for Real-World Robotic Manipulation

Abstract: For a robot to prepare a meal or clean a room, it must make a large array of decisions, such as what objects to clean first, where to grasp […]

Mar 28

March 28, 2024

11:00 am - 11:00 am

  • Hudson Hall 208

Abstract: For a robot to prepare a meal or clean a room, it must make a large array of decisions, such as what objects to clean first, where to grasp each ingredient and tool, how to open a heavy, overstuffed cabinet, and so on. To enable robots to tackle these tasks, I decompose the problem into two interdependent layers: generating a series of subgoals (i.e., a strategy) and solving for the robot behavior that achieves each of these subgoals. Critically, to accomplish a rich set of manipulation tasks, these subgoal solvers must account for force, motion, deformation, contact, uncertainty and partial observability.
My research contributes models and algorithms for generating this kind of robot behavior such that it both generalizes to new environments and can be composed into long-horizon strategies. In this talk, I will first discuss how this approach has enabled robots to perform tasks that require reasoning over and exerting force, like opening a childproof medicine bottle with a single arm. Next, I will illustrate different ways robots can make robust choices in the face of uncertainty. For example, this empowers robots to reliably chop up fruit of unknown ripeness! Finally, I propose how robots operating with uncertain dynamics can generate cautious behavior, such as shoving an object near the edge of the table without it falling.