Learning Visually Guided Latent Actions for Assistive Teleoperation

• URL: http://arxiv.org/abs/2105.00580v1
• Date: Sun, 2 May 2021 23:58:28 GMT
• Title: Learning Visually Guided Latent Actions for Assistive Teleoperation
• Authors: Siddharth Karamcheti, Albert J. Zhai, Dylan P. Losey, Dorsa Sadigh
• Abstract summary: We develop assistive robots that condition their latent embeddings on visual inputs. We show that incorporating object detectors pretrained on small amounts of cheap, easy-to-collect structured data enables i) accurately recognizing the current context and ii) generalizing control embeddings to new objects and tasks.
• Score: 9.75385535829762
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: It is challenging for humans -- particularly those living with physical disabilities -- to control high-dimensional, dexterous robots. Prior work explores learning embedding functions that map a human's low-dimensional inputs (e.g., via a joystick) to complex, high-dimensional robot actions for assistive teleoperation; however, a central problem is that there are many more high-dimensional actions than available low-dimensional inputs. To extract the correct action and maximally assist their human controller, robots must reason over their context: for example, pressing a joystick down when interacting with a coffee cup indicates a different action than when interacting with knife. In this work, we develop assistive robots that condition their latent embeddings on visual inputs. We explore a spectrum of visual encoders and show that incorporating object detectors pretrained on small amounts of cheap, easy-to-collect structured data enables i) accurately and robustly recognizing the current context and ii) generalizing control embeddings to new objects and tasks. In user studies with a high-dimensional physical robot arm, participants leverage this approach to perform new tasks with unseen objects. Our results indicate that structured visual representations improve few-shot performance and are subjectively preferred by users.

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