Qiang Li

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—The advent of sensor arrays providing tactile feedback with high spatial and temporal resolution asks for new control strategies to exploit this important and valuable sensory channel for grasping and manipulation tasks. In this paper, we introduce a control framework to realize a whole set of tactile servoing tasks, i.e. control tasks that intend to(More)
—In order to realize in-hand manipulation of unknown objects, we introduce an extension to our previously developed manipulation framework, such that long manipulation sequences, involving finger regrasping, become feasible. To this end, we propose a novel feedback controller, which searches for locally optimal contact points (suitable for regrasping),(More)
— We propose a feedback-based solution for the accurate manipulation of an unknown object in hand. This method does not explicitly take the friction and surface geometry of the manipulated object into consideration for the controller design, but employs a fast feedback loop based on visual and tactile feedback to perform robust manipulation even in the(More)
— We focus on manipulating the object of unknown shape, weight and friction properties in hand task. Unlike the traditional method considering the manipulation as multi times grasp planning, we follow the behavior robotics opinion and propose the reactive manipulation strategy. Firstly, we design the compact closed loop control (local controller) to(More)
We propose a simple but efficient control strategy to manipulate objects of unknown shape, weight, and friction properties – prerequisites which are necessary for classical offline grasping and manipulation methods. With this strategy, the object can be manipulated in hand in a large scale,(eg. to rotate the object 360 degree) regardless whether there is(More)
We propose a simple but efficient control strategy to manipulate objects of unknown shape, weight, and friction properties – prerequisites which are necessary for classical offline grasping and manipulation methods. Instead, the proposed control strategy employs estimated contact point locations, which can be obtained from modern tactile sensors with good(More)
— To augment traditionally vision-based body schema learning with a sensory channel that provides more accurate positional information, we propose a tactile-servoing feedback controller that allows a robot to continuously acquire self-touch information while sliding a fingertip across its own body. In this manner one can quickly acquire a large amount of(More)
— We present a novel hierarchical control framework that unifies our previous work on tactile-servoing with visual-servoing approaches to allow for robust manipulation and exploration of unknown objects, including – but not limited to – robust grasping, online grasp optimization, in-hand manipulation, and exploration of object surfaces. The framework is(More)
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