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Human-Robot Interaction
Machine Learning with Application to Robotics
Fast Adaptive Control
Dexterous Manipulation and Grasping
Computational Neuroscience and Cognitive Modeling

Dexterous manipulation and grasping

The human hand has incredible functionality. Our research focuses on understanding and replicating this capacity to robotic hands with very different kinematics.


We develop approaches and applications:
  • grasp planning: generate a variety of task-oriented grasps with hands by leveraging various learning strategies e.g., off-line optimization, learning from human demonstration
  • stable grasping: adapt the necessary force on-line to an object to achieve a safe grasp
  • dexterous manipulation: able to complete tasks with complicated dexterous manipulation
  • object interaction: learning an object manipulation strategy from human demonstration

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    Related publications

    I. Batzianoulis, S. El Khoury, E. Pirondini, M. Coscia and S. Micera et al. EMG-based decoding of grasp gestures in reaching-to-grasping motions, in Robotics and Autonomous Systems, vol. 91, p. 59-70, 2017.

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    K. Hang, J. A. Haustein, M. Li, A. Billard and C. Smith et al. On the Evolution of Fingertip Grasping Manifolds. IEEE International Conference on Robotics and Automation (ICRA), Royal Inst Technol, Ctr Autonomous Syst, Stockholm, SWEDEN, IEEE International Conference on Robotics and Automation ICRA, 2016.

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    N. Sommer and A. Billard. Multi-contact haptic exploration and grasping with tactile sensors, in Robotics and autonomous systems, vol. 85c, p. 48-61, 2016.

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    B. Huang, M. Li, R. L. De Souza, J. J. Bryson and A. Billard. A modular approach to learning manipulation strategies from human demonstration, in Autonomous Robots, vol. 40, num. 5, p. 903-927, 2016.

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    R. L. De Souza, A. Billard and J. Santos-Victor (Dirs.). Grasping for the Task: Human Principles for Robot Hands. EPFL, Lausanne, 2016.

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    M. Li, K. Hang, D. Kragic and A. Billard. Dexterous grasping under shape uncertainty, in Robotics and Autonomous Systems, vol. 75, p. 352-364, 2016.

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    B. Huang, M. Li, R. L. De Souza, J. J. Bryson and A. Billard. A Modular Approach to Learning Manipulation Strategies from Human Demonstration, in Autonomous Robots, p. 1-25, 2015.

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    K. J. A. Kronander, A. Billard (Dir.). Control and Learning of Compliant Manipulation Skills. EPFL, Lausanne, 2015.

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    K. Kronander and A. Billard. Learning Compliant Manipulation through Kinesthetic and Tactile Human-Robot Interaction, in Ieee Transactions On Haptics, vol. 7, num. 3, p. 367-380, 2014.

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    R. De Souza, S. El Khoury, J. Santos-Victor and A. Billard. Towards comprehensive capture of human grasping and manipulation skills. Thirteenth International Symposium on the 3-D Analysis of Human Movement, Lausanne, Switzerland, 2014.

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    N. Sommer, M. Li and A. Billard. Bimanual Compliant Tactile Exploration for Grasping Unknown Objects. IEEE International Conference on Robotics and Automation (ICRA), HongKong, China, 2014.

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    M. Li, H. Yin, K. Tahara and A. Billard. Learning Object-level Impedance Control for Robust Grasping and Dexterous Manipulation. IEEE International Conference on Robotics and Automation (ICRA), HongKong, China, 2014.

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    B. Huang, S. El Khoury, M. Li, J. J. Bryson and A. Billard. Learning a Real Time Grasping Strategy. 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, 2013.

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    R. Figueiredo, A. Shukla, D. Aragao, P. Moreno and A. Bernardino et al. Reaching and grasping kitchenware objects. International Symposium on System Integration, Fukuoka, Japan, 2012.

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    A. Shukla and A. Billard. Coupled dynamical system based arm-hand grasping model for learning fast adaptation strategies, in Robotics and Autonomous Systems, vol. 60, num. 3, p. 424-440, 2012.

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    F. D'halluin, A. de Rengervé, M. Lagarde, P. Gaussier and A. Billard et al. A state-action neural network supervising navigation and manipulation behaviors for complex task reproduction . Tenth International Conference on Epigenetic Robotics, Örenäs Slott, Sweden, 2010.

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    F. D'halluin, A. de Rengervé, M. Lagarde, P. Gaussier and A. Billard et al. A state-action neural network supervising navigation and manipulation behaviors for complex task reproduction . Tenth International Conference on Epigenetic Robotics, Örenäs Slott, Sweden, 2010.

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    E. Gribovskaya and A. Billard. Combining Task-Level and Trajectory-Level Learning for Teaching Robots Bimanual Coordinated Tasks. Robotics Science and Systems, Seattle, 2009.

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    S. Calinon, P. Evrard, E. Gribovskaya, A. Billard and A. Kheddar. Learning collaborative manipulation tasks by demonstration using a haptic interface. International Conference on Advanced Robotics (ICAR), Munich, Germany, 2009.

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    E. Gribovskaya and A. Billard. Combining Dynamical Systems Control and Programming by Demonstration for Teaching Discrete Bimanual Coordination Tasks to a Humanoid Robot. IEEE/ASM International Conference on Human-Robot Interaction, Amsterdam, Netherlands, 2008.

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    E. Gribovskaya and A. Billard. A Model of Acquisition of Discrete Bimanual Coordination Skills for a Humanoid Robot. International Conference in Epigenetic Robotics, 2007.

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    I. Goga and A. Billard. Development of goal-directed imitation, object manipulation and language in humans and robots, in In M. A. Arbib (ed.), Action to Language via the Mirror Neuron System, 2006.

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    M. Arbib, A. Billard, M. Iacobonni and E. Oztop. Synthetic brain imaging: grasping, mirror neurons and imitation, in Neural Networks, vol. 13, num. 8-9, p. 975-997, 2000.

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