CS Seminar: Abhishek Gupta, Techniques for Enabling Robotic Learning

Wednesday, February 24, 2021
1:00 p.m.
Online presentation--registration required

Computer Science Seminar

How to Train Your Robot: Techniques for Enabling Robotic Learning in the Real World

Abhishek Gupta
PhD student
University of California, Berkeley

URL (You’ll need to login with your umd.edu credentials): 
https://umd.zoom.us/j/94543765116?pwd=clY3MVV5Z1g4T2xpdnJMdjFiMFhYdz09

Abstract
Reinforcement learning has been a powerful tool for building continuously improving systems in domains like video games and animated character control, but has proven relatively more challenging to apply to problems in real world robotics. In this talk, I will argue that this challenge can be attributed to a mismatch in assumptions between typical RL algorithms and what the real world actually provides, making data collection and utilization difficult. In this talk, I will discuss how to build algorithms and systems to bridge these assumptions and allow robotic learning systems to operate under the assumptions of the real world. In particular, I will describe how we can develop algorithms to ensure easily scalable supervision from humans, perform safe, directed exploration in practical time scales and enable uninterrupted autonomous data collection at scale. I will show how these techniques can be applied to real world robotic systems and discuss how these have the potential to be applicable more broadly across a variety of machine learning applications. Lastly, I will provide some perspectives on how this opens the door towards future deployment of robots into unstructured human-centric environments.

Biography
Abhishek Gupta is a PhD student at UC Berkeley working with Pieter Abbeel and Sergey Levine, where he is interested in algorithms that can leverage reinforcement learning algorithms to solve robotics problems. He is interested in research directions that enable directly performing reinforcement learning directly in the real world — reward supervision in reinforcement learning, large scale real world data collection, learning from demonstrations, and multi-task reinforcement learning. He has also spent time at Google Brain. He is a recipient of the NDSEG and NSF graduate research fellowships, and several of his works have been presented as spotlight presentations at top-tier machine learning and robotics conferences. A more detailed description can be found at https://people.eecs.berkeley.edu/~abhigupta/

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