Real-time full body motion imitation on the COMAN humanoid robot

Florin Dzeladini, Auke Ijspeert, Jesse van den Kieboom
Cambridge Univ Press, 2015
Article

On-line full body imitation with a humanoid robot standing on its own two feet requires simultaneously maintaining the balance and imitating the motion of the demonstrator. In this paper we present a method that allows real-time motion imitation while maintaining stability, based on prioritized task control. We also describe a method of modified prioritized kinematic control that constrains the imitated motion to preserve stability only when the robot would tip over, but does not alter the motions otherwise. To cope with the passive compliance of the robot, we show how to model the estimation of the center of mass of the robot using support vector machines. In the paper we give detailed description of all steps of the algorithm, essentially providing a tutorial on the implementation of kinematic stability control. We present the results on a child-sized humanoid robot called Compliant Humanoid Platform or COMAN. Our implementation shows reactive and stable on-line motion imitation of the humanoid robot.

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Florin Dzeladini, Auke Ijspeert, Jesse van den Kieboom
Cambridge Univ Press, 2015
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https://infoscience.epfl.ch/record/202083?ln=fr

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Bio-inspired balance controller for a humanoid robot

François André Heremans, Auke Ijspeert, Renaud Ronsse, Nicolas Benoît Dominique Van der Noot

Humanoid robots are gaining much interest nowadays. This is partly motivated by the ability of such robots to replace humans in dangerous environments being specifically designed for humans, such as man-made or natural disaster scenarios. However, existing robots are far from reaching human skills regarding the robustness to external perturbations required for such tasks, although torque-controlled and even bio-inspired robots hold new promises for research. A humanoid robot robustly interacting with its environment should be capable of handling highly uncertain ground structures, collisions, and other external perturbations. In this paper, a 3D bio-inspired balance controller is developed using a virtual lower limbs musculoskeletal model. An inverse muscular model that transforms the desired torque patterns into muscular stimulations closes the gap between traditional and bio-inspired controllers. The main contribution consists in developing a neural controller that computes the muscular stimulations driving this musculoskeletal model. This neural controller exploits the inverse model output to progressively learn the appropriate muscular stimulations for rejecting disturbances, without relying on the inverse model anymore. Two concurrent approaches are implemented to perform this autonomous learning: a cerebellar model and a support vector regression algorithm. The developed methods are tested in the Robotran simulation environment with COMAN, a compliant child-sized humanoid robot. Results illustrate that - at the end of the learning phase - the robot manages to reject perturbations by performing a full-body compensation requiring neither to solve an inverse dynamic model nor to get force measurement. Muscular stimulations are directly generated based on the previously learned perturbations.

Ieee2016

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When Positive Perception of the Robot Has No Effect on Learning

Barbara Bruno, Pierre Dillenbourg, Jauwairia Nasir, Utku Norman

Humanoid robots, with a focus on personalised social behaviours, are increasingly being deployed in educational settings to support learning. However, crafting pedagogical HRI designs and robot interventions that have a real, positive impact on participants' learning, as well as effectively measuring such impact, is still an open challenge. As a first effort in tackling the issue, in this paper we propose a novel robot-mediated, collaborative problem solving activity for school-children, called JUSThink, aiming at improving their computational thinking skills. JUSThink will serve as a baseline and reference for investigating how the robot's behaviour can influence the engagement of the children with the activity, as well as their collaboration and mutual understanding while working on it. To this end, this first iteration aims at investigating (i) participants' engagement with the activity (Intrinsic Motivation Inventory-IMI), their mutual understanding (IMI-like) and perception of the robot (Godspeed Questionnaire); (ii) participants' performance during the activity, using several performance and learning metrics. We carried out an extensive user-study in two international schools in Switzerland, in which around 100 children participated in pairs in one-hour long interactions with the activity. Surprisingly, we observe that while a teams' performance significantly affects how team members evaluate their competence, mutual understanding and task engagement, it does not affect their perception of the robot and its helpfulness, a fact which highlights the need for baseline studies and multi-dimensional evaluation metrics when assessing the impact of robots in educational activities.

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