Conference proceeding
Applying Human Motion Capture to Design Energy-efficient Trajectories for Miniature Humanoids
2012 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), pp 3425-3431
01 Jan 2012
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
In this research, an approach to optimize motions for a humanoids is presented. Rapidly-exploring Random Tree(RRT) were used to plan an initial suboptimal motion. A reinforcement learning was then implemented to optimize the trajectories with respect to energy consumption, similarity to a human's natural motion and, physical limits. Energy cost was estimated by joint torque from a dynamic model, and validated against actual measured torque values using system identification (SID). With a motion capture system, human motions were collected for a given set of tasks, producing a representative "natural" motion, another cost for optimization. Physical limits of each joint ensured spatial and temporal smoothness of generated trajectories. Finally, an experimental evaluation of the presented approach was demonstrated through simulation using MiniHubo model in OpenRAVE.
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Details
- Title
- Applying Human Motion Capture to Design Energy-efficient Trajectories for Miniature Humanoids
- Creators
- Kiwon Sohn - Drexel UniversityPaul Oh - Drexel UniversityRobotics Society of Japan
- Publication Details
- 2012 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), pp 3425-3431
- Series
- IEEE International Conference on Intelligent Robots and Systems
- Publisher
- IEEE
- Number of pages
- 7
- Resource Type
- Conference proceeding
- Language
- English
- Web of Science ID
- WOS:000317042703156
- Scopus ID
- 2-s2.0-84872285873
- Other Identifier
- 991019348903404721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Automation & Control Systems
- Computer Science, Artificial Intelligence
- Robotics