Flexible Vision-Based Robotic Manipulation via Meta-Learning and Deep Reinforcement Learning

Abstract

Approved for Public ReleaseRobots that are autonomous and versatile have the potential to be tremendously useful for a range of manu al, tedious, or dangerous tasks that are currently performed entirely by humans. However, in order to be successful in performing ma nipulation tasks in diverse, dynamic, and unstructured environments, robots must be able to learn continuously, across tasks, enviro nments, and even experimental set-ups, perpetually accumulating their experiences and learning from them. This setting, however, pre sents massive challenges for current robot learning approaches, which largely assume carefully-designed experimental set-ups and sta te representations, narrow single-task learning problems, and supervision in the form of reward feedback or labels. In this project, we aim to explore how we can enable robots to learn continuously from raw perception inputs. In particular, we will address the fol lowing questions. Can algorithms leverage large amounts of diverse data, across environments and experiments, to acquire skills that generalize broadly? Can robots leverage large datasets of videos of human behavior to broaden the generalization of behaviors and g oal representations? How can algorithms leverage compositional structure to learn increasingly complex tasks over time? We will stud y each of these questions in the context of three research themes: first, how visual world models can be learned across diverse, mul ti-domain data including videos of humans for solving a breadth of tasks; second, how meta-learning can be used to continuous learn a sequence of tasks including a curriculum of compositional tasks; and, third, how robots can develop internal representations of ta sks from diverse data sources.

Document Details

Document Type
DoD Grant Award
Publication Date
Sep 08, 2021
Source ID
N000142112685

Entities

People

  • Chelsea Finn

Organizations

  • Office of Naval Research
  • Stanford University
  • United States Navy

Tags

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Distributed Systems and Data Platform Development
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Autonomy