Whole-Body Contact Force Sensing From Motion Capture

Tu-Hoa Pham, Adrien Bufort, Stéphane Caron and Abderrahmane Kheddar. SII 2016, Sapporo, Japan, December 2016. Best Paper Award.


In this paper, we challenge the estimation of contact forces backed with ground-truth sensing in human whole-body interaction with the environment, from motion capture only. Our novel method makes it possible to get rid of cumbersome force sensors in monitoring multi-contact motion together with force data. This problem is very challenging. Indeed, while a given force distribution uniquely determines the resulting kinematics, the converse is generally not true in multi-contact. In such scenarios, physics-based optimization alone may only capture force distributions that are physically compatible with a given motion rather than the actual forces being applied. We address this indeterminacy by collecting a large-scale dataset on whole-body motion and contact forces humans apply in multi-contact scenarios. We then train recurrent neural networks on real human force distribution patterns and complement them with a second-order cone program ensuring the physical validity of the predictions. Extensive validation on challenging dynamic and multi-contact scenarios shows that the method we propose can outperform physical force sensing both in terms of accuracy and usability.


  title = {Whole-Body Contact Force Sensing From Motion Capture},
  author = {Pham, Tu-Hoa and Bufort, Adrien and Caron, St{\'e}phane and Kheddar, Abderrahmane},
  booktitle = {Proceedings of the 2016 IEEE/SICE International Symposium on System Integration},
  year = {2016},
  month = dec,
  pages = {58--63},
  organization = {IEEE/SICE},
  doi = {10.1109/SII.2016.7843975},
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