Sonia Chernova

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Wepresent a comprehensive survey of robot Learning fromDemonstration (LfD), a technique that develops policies from example state to action mappings. We introduce the LfD design choices in terms of demonstrator, problem space, policy derivation and performance, and contribute the foundations for a structure in which to categorize LfD research. Specifically,(More)
We present Confidence-Based Autonomy (CBA), an interactive algorithm for policy learning from demonstration. The CBA algorithm consists of two components which take advantage of the complementary abilities of humans and computer agents. The first component, Confident Execution, enables the agent to identify states in which demonstration is required, to(More)
Developing fast gaits for legged robots is a difficult task that requires optimizing parameters in a highly irregular, multidimensional space. In the past, walk optimization for quadruped robots, namely the Sony AIBO robot, was done by handtuning the parameterized gaits. In addition to requiring a lot of time and human expertise, this process produced(More)
We contribute an approach for interactive policy learning through expert demonstration that allows an agent to actively request and effectively represent demonstration examples. In order to address the inherent uncertainty of human demonstration, we represent the policy as <i>a set of Gaussian mixture models</i> (GMMs), where each model, with multiple(More)
The Interactive Reinforcement Learning algorithm enables a human user to train a robot by providing rewards in response to past actions and anticipatory guidance to guide the selection of future actions. Past work with software agents has shown that incorporating user guidance into the policy learning process through Interactive Reinforcement Learning(More)
In support of a feasibility study of reproductive and developmental health among females employed in the Monchegorsk (Russia) nickel refinery, personal exposure and biological monitoring assessments were conducted. The inhalable aerosol fraction was measured and characterised by chemical speciation and particle-size distribution measurements. Unexpected(More)
Reinforcement learning describes how a learning agent can achieve optimal behaviour based on interactions with its environment and reward feedback. A limiting factor in reinforcement learning as employed in artificial intelligence is the need for an often prohibitively large number of environment samples before the agent reaches a desirable level of(More)
Effective learning from demonstration techniques enable complex robot behaviors to be taught from a small number of demonstrations. A number of recent works have explored interactive approaches to demonstration, in which both the robot and the teacher are able to select training examples. In this paper, we focus on a demonstration selection algorithm used(More)
Human-Robot Interaction (HRI) is a rapidly expanding field of study that focuses on allowing nonroboticist users to naturally and effectively interact with robots. The importance of conducting extensive user studies has become a fundamental component of HRI research; however, due to the nature of robotics research, such studies often become expensive, time(More)