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Perception, motor learning, and speed adaptation exploiting body dynamics: case studies in a quadruped robot


Hoffmann, Matej; Schmidt, Nico; Nakajima, Kohei; Iida, Fumiya; Pfeifer, Rolf (2011). Perception, motor learning, and speed adaptation exploiting body dynamics: case studies in a quadruped robot. In: 5th International Symposium on Adaptive Motion of Animals and Machines (AMAM), Hyogo, Japan, 11 October 2011 - 14 October 2011, 39-40.

Abstract

Animals and humans are constantly faced with a highly dimensional stream of incoming sensory information. At the same time, they have to command their highly complex and multidimensional bodies. Yet, they seamlessly cope with this situation and successfully perform various tasks. For autonomous robots, this poses a challenge: robots performing in the real world are often faced with the curse of dimensionality. In other words, the size of the sensory as well as motor spaces becomes too large for the robot to efficiently cope with them in real time. In this paper, we demonstrate how the curse of dimensionality can be tamed by exploiting the robot’s morphology and interaction with the environment, or the robot’s embodiment (see e.g., [1]). We present three case studies with underactuated quadrupedal robots. In the first case study, we look at terrain detection. While running on different surfaces, the robot generates structured multimodal sensory information that can be used to detect different terrain types. In the second case study, we shift our attention to the motor space: the robot is learning different gaits. The online learning procedure capitalizes on the fact that the robot is underactuated and on a “soft“ control policy. In the third case study, we move one level higher and demonstrate how - given an appropriate gait - a speed adaptation task can be greatly simplified and learned online.

Animals and humans are constantly faced with a highly dimensional stream of incoming sensory information. At the same time, they have to command their highly complex and multidimensional bodies. Yet, they seamlessly cope with this situation and successfully perform various tasks. For autonomous robots, this poses a challenge: robots performing in the real world are often faced with the curse of dimensionality. In other words, the size of the sensory as well as motor spaces becomes too large for the robot to efficiently cope with them in real time. In this paper, we demonstrate how the curse of dimensionality can be tamed by exploiting the robot’s morphology and interaction with the environment, or the robot’s embodiment (see e.g., [1]). We present three case studies with underactuated quadrupedal robots. In the first case study, we look at terrain detection. While running on different surfaces, the robot generates structured multimodal sensory information that can be used to detect different terrain types. In the second case study, we shift our attention to the motor space: the robot is learning different gaits. The online learning procedure capitalizes on the fact that the robot is underactuated and on a “soft“ control policy. In the third case study, we move one level higher and demonstrate how - given an appropriate gait - a speed adaptation task can be greatly simplified and learned online.

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Additional indexing

Item Type:Conference or Workshop Item (Speech), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:14 October 2011
Deposited On:03 Jan 2014 07:51
Last Modified:05 Apr 2016 17:17
Publisher:Tohoku University
Series Name:International Symposium on Adaptive Motion of Animals and Machines (AMAM 2011)
Number:5
Free access at:Official URL. An embargo period may apply.
Official URL:http://adaptivemotion.org/AMAM2011/papers/s221.pdf
Related URLs:http://adaptivemotion.org/AMAM2011 (Publisher)
Other Identification Number:merlin-id:8706
Permanent URL: https://doi.org/10.5167/uzh-86932

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