Learning and grounding haptic affordances using demonstration and human-guided exploration

摘要

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.