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Socially Intelligent Machines Lab

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Author: Cakmak, Maya × End date: 2014 × Start date: 2010 ×

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Now showing 1 - 7 of 7
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Keyframe-based Learning from Demonstration Method and Evaluation

2012-06 , Akgun, Baris , Cakmak, Maya , Jiang, Karl , Thomaz, Andrea L.

We present a framework for learning skills from novel types of demonstrations that have been shown to be desirable from a human-robot interaction perspective. Our approach –Keyframe-based Learning from Demonstration (KLfD)– takes demonstrations that consist of keyframes; a sparse set of points in the state space that produces the intended skill when visited in sequence. The conventional type of trajectory demonstrations or a hybrid of the two are also handled by KLfD through a conversion to keyframes. Our method produces a skill model that consists of an ordered set of keyframe clusters, which we call Sequential Pose Distributions (SPD). The skill is reproduced by splining between clusters. We present results from two domains: mouse gestures in 2D and scooping, pouring and placing skills on a humanoid robot. KLfD has performance similar to existing LfD techniques when applied to conventional trajectory demonstrations. Additionally, we demonstrate that KLfD may be preferable when demonstration type is suited for the skill.

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Designing Interactions for Robot Active Learners

2010-06 , Cakmak, Maya , Chao, Crystal , Thomaz, Andrea L.

This paper addresses some of the problems that arise when applying active learning to the context of human–robot interaction (HRI). Active learning is an attractive strategy for robot learners because it has the potential to improve the accuracy and the speed of learning, but it can cause issues from an interaction perspective. Here we present three interaction modes that enable a robot to use active learning queries. The three modes differ in when they make queries: the first makes a query every turn, the second makes a query only under certain conditions, and the third makes a query only when explicitly requested by the teacher.We conduct an experiment in which 24 human subjects teach concepts to our upper-torso humanoid robot, Simon, in each interaction mode, and we compare these modes against a baseline mode using only passive supervised learning.We report results from both a learning and an interaction perspective. The data show that the three modes using active learning are preferable to the mode using passive supervised learning both in terms of performance and human subject preference, but each mode has advantages and disadvantages. Based on our results, we lay out several guidelines that can inform the design of future robotic systems that use active learning in an HRI setting.

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Exploiting social partners in robot learning

2010 , Cakmak, Maya , DePalma, Nick , Arriaga, Rosa I. , Thomaz, Andrea L.

Social learning in robotics has largely focused on imitation learning. Here we take a broader view and are interested in the multifaceted ways that a social partner can in uence the learning process. We implement four social learning mechanisms on a robot: stimulus enhancement, emulation, mimicking, and imitation, and illustrate the computational benefits of each. In particular, we illustrate that some strategies are about directing the attention of the learner to objects and others are about actions. Taken together these strategies form a rich repertoire allowing social learners to use a social partner to greatly impact their learning process. We demonstrate these results in simulation and with physical robot `playmates'.

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Trajectories and Keyframes for Kinesthetic Teaching: A Human-Robot Interaction Perspective

2012-03 , Akgun, Baris , Cakmak, Maya , Yoo, Jae Wook , Thomaz, Andrea L.

Kinesthetic teaching is an approach to providing demonstrations to a robot in Learning from Demonstration whereby a human physically guides a robot to perform a skill. In the common usage of kinesthetic teaching, the robot's trajectory during a demonstration is recorded from start to end. In this paper we consider an alternative, keyframe demonstrations, in which the human provides a sparse set of consecutive keyframes that can be connected to perform the skill. We present a user-study (n = 34) comparing the two approaches and highlighting their complementary nature. The study also tests and shows the potential benefits of iterative and adaptive versions of keyframe demonstrations. Finally, we introduce a hybrid method that combines trajectories and keyframes in a single demonstration

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Optimality of Human Teachers for Robot Learners

2010 , Cakmak, Maya , Thomaz, Andrea L.

In this paper we address the question of how closely everyday human teachers match a theoretically optimal teacher. We present two experiments in which subjects teach a concept to our robot in a supervised fashion. In the first experiment we give subjects no instructions on teaching and observe how they teach naturally as compared to an optimal strategy. We find that people are suboptimal in several dimensions. In the second experiment we try to elicit the optimal teaching strategy. People can teach much faster using the optimal teaching strategy, however certain parts of the strategy are more intuitive than others.

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Towards Grounding Concepts for Transfer in Goal Learning from Demonstration

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.

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Transparent Active Learning for Robots

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.

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