(Georgia Institute of Technology, 2011-08)
Chao, Crystal; Cakmak, Maya; Thomaz, Andrea L.
We aim to build robots that frame the task learning problem as goal inference so that they are natural to teach and meet people's expectations for a learning partner. The focus of this work is the scenario of a social robot that learns task goals from human demonstrations without prior knowledge of high-level concepts. In the system that we present, these discrete concepts are grounded from low-level continuous sensor data through unsupervised learning, and task goals are subsequently learned on them using Bayesian inference. The grounded concepts are derived from the structure of the Learning from Demonstration (LfD) problem and exhibit degrees of prototypicality. These concepts can be used to transfer knowledge to future tasks, resulting in faster learning of those tasks. Using sensor data taken during demonstrations to our robot from five human teachers, we show the expressivity of using grounded concepts when learning new tasks from demonstration. We then show how the learning curve improves when transferring the knowledge of grounded concepts to future tasks.
(Georgia Institute of Technology, 2011)
Chao, Crystal; Lee, Jinhan; Begum, Momotaz; Thomaz, Andrea L.
Turn-taking is fundamental to the way humans engage in information exchange, but robots currently lack the turn-taking skills required for natural communication. In order to bring effective turn-taking to robots, we must first understand the underlying processes in the context of what is possible to implement. We describe a data collection experiment with an interaction format inspired by “Simon says,” a turn-taking imitation game that engages the channels of gaze, speech, and motion. We analyze data from 23 human subjects interacting with a humanoid social robot and propose the principle of minimum necessary information (MNI) as a factor in determining the timing of the human response.We also describe the other observed phenomena of channel exclusion, efficiency, and adaptation. We discuss the implications of these principles and propose some ways to incorporate our findings into a computational model of turn-taking.
(Georgia Institute of Technology, 2010)
Chao, Crystal; Cakmak, Maya; Thomaz, Andrea L.
This research aims to enable robots to learn from
human teachers. Motivated by human social learning, we believe
that a transparent learning process can help guide the human
teacher to provide the most informative instruction. We believe
active learning is an inherently transparent machine learning approach
because the learner formulates queries to the oracle that
reveal information about areas of uncertainty in the underlying
model. In this work, we implement active learning on the Simon
robot in the form of nonverbal gestures that query a human
teacher about a demonstration within the context of a social
dialogue. Our preliminary pilot study data show potential for
transparency through active learning to improve the accuracy
and efficiency of the teaching process. However, our data also
seem to indicate possible undesirable effects from the human
teacher’s perspective regarding balance of the interaction. These
preliminary results argue for control strategies that balance
leading and following during a social learning interaction.