Standing Self-Manipulation for a Legged Robot

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General Robotics, Automation, Sensing and Perception Laboratory
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Electrical and Computer Engineering
Systems Engineering
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Johnson, Aaron M.
Haynes, G. Clark

On challenging, uneven terrain a legged robot’s open loop posture will almost inevitably be inefficient, due to uncoordinated support of gravitational loads with coupled internal torques. By reasoning about certain structural properties governing the infinitesimal kinematics of the closed chains arising from a typical stance, we have developed a computationally trivial self-manipulation behavior that can minimize both internal and external torques absent any terrain information. The key to this behavior is a change of basis in torque space that approximates the partially decoupled nature of the two types of disturbances. The new coordinates reveal how to use actuator current measurements as proprioceptive sensors for the approximate gradients of both the internal and external task potential fields, without recourse to further modeling. The behavior is derived using a manipulation framework informed by the dual relationship between a legged robot and a multifingered hand. We implement the reactive posture controller resulting from simple online descent along these proprioceptively sensed gradients on the X-RHex robot to document the significant savings in standing power. For more information: Kod*Lab

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BibTeX entry @inproceedings{paper:johnson-iros-2012, author = {Aaron M. Johnson and G Clark Haynes and D E Koditschek}, title = {Standing Self-Manipulation for a Legged Robot}, booktitle = {Proceedings of the IEEE/RSJ Intl. Conference on Intelligent Robots and Systems}, month = {October}, year = {2012}, address = {Algarve, Portugal}, pages = {272--279} } Copyright YEAR 2012. Reprinted from Proceedings of the 2012 IEEE/RSJ Intl. Conference on Intelligent Robots and Systems, pages 272-279. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
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