Shared autonomy via hindsight optimization for teleoperation and teaming
Abstract
In shared autonomy, a user and autonomous system work together to achieve shared goals. To collaborate effectively, the autonomous system must know the user’s goal. As such, most prior works follow a predict-then-act model, first predicting the user’s goal with high confidence, then assisting given that goal. Unfortunately, confidently predicting the user’s goal may not be possible until they have nearly achieved it, causing predict-then-act methods to provide little assistance. However, the system can often provide useful assistance even when confidence for any single goal is low (e.g. move towards multiple goals). In this work, we formalize this insight by modeling shared autonomy as a partially observable Markov decision process (POMDP), providing assistance that minimizes the expected cost-to-go with an unknown goal. As solving this POMDP optimally is intractable, we use hindsight optimization to approximate. We apply our framework to both shared-control teleoperation and human–robot teaming. Compared with predict-then-act methods, our method achieves goals faster, requires less user input, decreases user idling time, and results in fewer user–robot collisions.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 01, 2018
- Source ID
- 10.1177/0278364918776060
Entities
People
- Henny Admoni
- J. Andrew Bagnell
- Shervin Javdani
- Siddhartha Srinivasa
- Stefania Pellegrinelli
Organizations
- Carnegie Mellon University
- Defense Advanced Research Projects Agency
- Division of Computer and Network Systems
- Division of Information and Intelligent Systems
- National Science Foundation
- Office of Naval Research
- Okawa Foundation for Information and Telecommunications