Issues in dextrous robot hands
Dextrous robot hands
On the closure properties of robotic grasping
International Journal of Robotics Research
Incremental learning of gestures by imitation in a humanoid robot
Proceedings of the ACM/IEEE international conference on Human-robot interaction
Bio-inspired grasp control in a robotic hand with massive sensorial input
Biological Cybernetics
Active learning with statistical models
Journal of Artificial Intelligence Research
Contact sensing and grasping performance of compliant hands
Autonomous Robots
Reactive grasping using optical proximity sensors
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Robust sensor-based grasp primitive for a three-finger robot hand
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
A strategy for grasping unknown objects based on co-planarity and colour information
Robotics and Autonomous Systems
Tactile sensing: from humans to humanoids
IEEE Transactions on Robotics
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Combining active learning and reactive control for robot grasping
Robotics and Autonomous Systems
Learning Non-linear Multivariate Dynamics of Motion in Robotic Manipulators
International Journal of Robotics Research
Tactile Guidance for Policy Adaptation
Foundations and Trends in Robotics
On Learning, Representing, and Generalizing a Task in a Humanoid Robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In the context of object interaction and manipulation, one characteristic of a robust grasp is its ability to comply with external perturbations applied to the grasped object while still maintaining the grasp. In this work, we introduce an approach for grasp adaptation which learns a statistical model to adapt hand posture solely based on the perceived contact between the object and fingers. Using a multi-step learning procedure, the model dataset is built by first demonstrating an initial hand posture, which is then physically corrected by a human teacher pressing on the fingertips, exploiting compliance in the robot hand. The learner then replays the resulting sequence of hand postures, to generate a dataset of posture-contact pairs that are not influenced by the touch of the teacher. A key feature of this work is that the learned model may be further refined by repeating the correction-replay steps. Alternatively, the model may be reused in the development of new models, characterized by the contact signatures of a different object. Our approach is empirically validated on the iCub robot. We demonstrate grasp adaptation in response to changes in contact, and show successful model reuse and improved adaptation with additional rounds of model refinement.