Learning and Grounding Haptic Affordances Using Demonstration and Human-Guided Exploration
Abstract
We present a system for learning haptic affordance models of complex manipulation skills. The goal of a haptic affordance model is to improve task completion by characterizing the feel of a particular object-action pair. We use learning from demonstration to provide the robot with an example of a successful interaction with a given object. We then use environmental scaffolding and a wrist-mounted force/torque (F/T) sensor to collect grounded examples (successes and unsuccessful "near misses") of the haptic data for the object-action pair. From this, we build one "success" Hidden Markov Model (HMM) and one "near-miss" HMM for each object-action pair. We evaluate this approach with five different actions on seven different objects to learn two specific affordances (open-able and scoop-able). We show that by building a library of object-action pairs for each affordance, we can successfully monitor a trajectory of haptic data to determine if the robot finds an affordance.
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Index Terms
- Learning and Grounding Haptic Affordances Using Demonstration and Human-Guided Exploration
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Information & Contributors
Information
Published In
March 2016
676 pages
ISBN:9781467383707
- General Chairs:
- Christoph Bartneck,
- Yukie Nagai,
- Program Chairs:
- Ana Paiva,
- Selma Šabanović
Sponsors
- SIGAI: ACM Special Interest Group on Artificial Intelligence
- RA: IEEE Robotics and Automation Society
- SIGCHI: ACM Special Interest Group on Computer-Human Interaction
In-Cooperation
- AAAI: American Association for Artificial Intelligence
- Human Factors & Ergonomics Soc: Human Factors & Ergonomics Soc
Publisher
IEEE Press
Publication History
Published: 07 March 2016
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- Poster
Funding Sources
- Office of Naval Research Grant
Conference
HRI '16: ACM/IEEE International Conference on Human-Robot Interaction
March 7 - 10, 2016
Christchurch, New Zealand
Acceptance Rates
HRI '16 Paper Acceptance Rate 45 of 181 submissions, 25%;
Overall Acceptance Rate 268 of 1,124 submissions, 24%
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