In the canonical Robot learning from Demonstration scenario a robot observes performances of a task and then develops an autonomous controller. Current work acknowledges that humans may be suboptimal demonstrators and...
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In the canonical Robot learning from Demonstration scenario a robot observes performances of a task and then develops an autonomous controller. Current work acknowledges that humans may be suboptimal demonstrators and refines the controller for improved performance. However, there is still an assumption that the demonstrations are successful examples of the task. We here consider the possibility that the human has failed, and propose a model to minimize the possibility of the robot making the same mistakes.
Behavior adaptation with execution experience is a practical feature for any policy learning system. Our work provides performance feedback to a robot learner in the form of tactile corrections from a human teacher, f...
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Behavior adaptation with execution experience is a practical feature for any policy learning system. Our work provides performance feedback to a robot learner in the form of tactile corrections from a human teacher, for the purpose of policy refinement as well as policy reuse. Multiple variants of our general approach have been validated on the iCub robot, as building blocks towards a high-DoF humanoid system that integrates tactile sensing on the hands and arms into complex behaviors and sophisticated learning routines.
This article combines programming by demon- stration and adaptive control for teaching a robot to physically interact with a human in a collaborative task requiring sharing of a load by the two partners. learning a ta...
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This article combines programming by demon- stration and adaptive control for teaching a robot to physically interact with a human in a collaborative task requiring sharing of a load by the two partners. learning a task model allows the robot to anticipate the partner’s intentions and adapt its motion according to perceived forces. As the human represents a highly complex contact environment, direct reproduction of the learned model may lead to sub-optimal results. To compen- sate for unmodelled uncertainties, in addition to learning we propose an adaptive control algorithm that tunes the impedance parameters, so as to ensure accurate reproduction. To facilitate the illustration of the concepts introduced in this paper and provide a systematic evaluation, we present experimental results obtained with simulation of a dyad of two planar 2-DOF robots.
Over the years, robots have been developed to help humans in their everyday life, from preparing food, to autism therapy [2]. To accomplish their tasks, in addition to their engineered skills, today's robots are n...
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Over the years, robots have been developed to help humans in their everyday life, from preparing food, to autism therapy [2]. To accomplish their tasks, in addition to their engineered skills, today's robots are now learning from observing humans, from interacting with them [1]. Therefore, one may expect that one day, robots may develop a form of consciousness, and a desire for freedom. Hopefully, this desire will come with a wish for robots, to become an integral part of our human society. Until we can test this hypothesis, we present a fictional adventure of our robot friends: During an official human-robot interaction challenge, Keepon [2] and Chief Cook (a.k.a. Hoap-3) [1] decided to escape their original duties and joined their forces to drive humans into an entertaining and interactive activity that they often forget to practice: Dancing. Indeed, is there any better way for robots to establish a solid communication channel with humans, so that the traditional master-slave relation may turn into friendship?
In this paper, we report on a study on gaze behavior by children with Autism Spectrum Disorder (ASD) during a dyadic interaction in a naturalistic environment. Twelve children with ASD were contrasted to twelve typica...
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ISBN:
(纸本)9781424441211
In this paper, we report on a study on gaze behavior by children with Autism Spectrum Disorder (ASD) during a dyadic interaction in a naturalistic environment. Twelve children with ASD were contrasted to twelve typically developing (TD) children, in a semi-structured interaction with a selection of items from the Early Social Communication Scale (ESCS). We used the WearCam, a novel head-mounted eye-tracker designed for children, to obtain gaze information across the broad field of view from the viewpoint of the child. Children with ASD looked downwards more often, and explored their lateral field of view more extensively compared to TD children. We discuss a number of hypotheses in support of these observations.
We report on trace gas and major atmospheric constituents results obtained by the Vehicle Cabin Atmosphere Monitor (VCAM) during operations aboard the International Space Station (ISS). VCAM is an autonomous environme...
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Demonstration learning is a powerful and practical technique to develop robot behaviors. Even so, development remains a challenge and possible demonstration limitations can degrade policy performance. This work presen...
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Demonstration learning is a powerful and practical technique to develop robot behaviors. Even so, development remains a challenge and possible demonstration limitations can degrade policy performance. This work presents an approach for policy improvement and adaptation through a tactile interface located on the body of a robot. We introduce the Tactile Policy Correction (TPC) algorithm, that employs tactile feedback for the refinement of a demonstrated policy, as well as its reuse for the development of other policies. We validate TPC on a humanoid robot performing grasp-positioning tasks. The performance of the demonstrated policy is found to improve with tactile corrections. Tactile guidance also is shown to enable the development of policies able to successfully execute novel, undemonstrated, tasks.
We present a probabilistic architecture for solving generically the problem of extracting the task constraints through a programming by demonstration framework and for generalizing the acquired knowledge to various si...
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This paper presents the application of a statistical framework that allows to endow a humanoid robot with the ability to perform a collaborative manipulation task with a human operator. We investigate to what extent t...
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We present a generic framework that allows learning non-linear dynamics of motion in manipulation tasks and generating dynamical laws for control of position and orientation. This work follows a recent trend in Progra...
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ISBN:
(纸本)9781424445875
We present a generic framework that allows learning non-linear dynamics of motion in manipulation tasks and generating dynamical laws for control of position and orientation. This work follows a recent trend in Programming by Demonstration in which the dynamics of an arm motion is learned: position and orientation control are learned as multi-variate dynamical systems to preserve correlation within the signals. The strength of the method is three-fold: (i) it extracts dynamical control laws from demonstrations, and subsequently provides concurrent smooth control of both position and orientation; (ii) it allows to generalize a motion to unseen context; (iii) it guarantees on-line adaptation of the motion in the face of spatial and temporal perturbations. The method is validated to control a four degree of freedom humanoid arm and an industrial six degree of freedom robotic arm.
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