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Learning for Tactile Manipulation
Tucker Hermans · Filipe Veiga · Janine Hölscher · Herke van Hoof · Jan Peters

Wed Dec 10 04:00 PM -- 08:59 PM (PST) @ Level 2, room 230B

Tactile sensing affords robots the opportunity to dexterously manipulate objects in-hand without the need of strong object models and planning. Our demonstration focuses on learning for tactile, in-hand manipulation by robots. We address learning problems related to the control of objects in-hand, as well as perception problems encountered by a robot exploring its environment with a tactile sensor. We demonstrate applications for three specific learning prob- lems: learning to detect slip for grasp stability, learning to reposition objects in-hand, and learning to identify objects and object properties through tactile exploration. We address the problem of learning to detect slip of grasped objects. We show that the robot can learn a detector for slip events which generalizes to novel objects. We leverage this slip detector to produce a feedback controller that can stabilize objects during grasping and manipulation. Our work compares a number of supervised learning approaches and feature representations in order to achieve reliable slip detection. Tactile sensors provide observations of high enough dimension to cause prob- lems for traditional reinforcement learning methods. As such, we introduce a novel reinforcement learning (RL) algorithm which learns transition functions embedded in a reproducing kernel Hilbert space (RKHS). The resulting policy search algorithm provides robust policy updates which can efficiently deal with high-dimensional sensory input. We demonstrate the method on the problem of repositioning a grasped object in the hand. Finally, we present a method for learning to classify objects through tactile exploration. The robot collects data from a number of objects through various exploratory motions. The robot learns a classifier for each object to be used dur- ing exploration of its environment to detect objects in cluttered environments. Here again we compare a number of learning methods and features present in the literature and synthesize a method to best work in human environments the robot is likely to encounter. Users will be able to interact with a robot hand by giving it objects to grasp and attempting to remove these objects from the robot. The hand will also perform some basic in-hand manipulation tasks such as rolling the object between the fingers and rotating the object about a fixed grasp point. Users will also be able to interact with a touch sensor capable of classifying objects as well as semantic events such as slipping from a stable contact location.

Author Information

Tucker Hermans (TU Darmstadt)
Filipe Veiga (TU Darmstadt)
Janine Hölscher (TU Darmstadt)
Herke van Hoof (TU Darmstadt)
Jan Peters (TU Darmstadt & DFKI)

Jan Peters is a full professor (W3) for Intelligent Autonomous Systems at the Computer Science Department of the Technische Universitaet Darmstadt and at the same time a senior research scientist and group leader at the Max-Planck Institute for Intelligent Systems, where he heads the interdepartmental Robot Learning Group. Jan Peters has received the Dick Volz Best 2007 US PhD Thesis Runner-Up Award, the Robotics: Science & Systems - Early Career Spotlight, the INNS Young Investigator Award, and the IEEE Robotics & Automation Society‘s Early Career Award as well as numerous best paper awards. In 2015, he was awarded an ERC Starting Grant. Jan Peters has studied Computer Science, Electrical, Mechanical and Control Engineering at TU Munich and FernUni Hagen in Germany, at the National University of Singapore (NUS) and the University of Southern California (USC). He has received four Master‘s degrees in these disciplines as well as a Computer Science PhD from USC.

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