We present a new method to reconstruct the shape of an unknown object using tactile sensors without requiring object immobilization. Instead, the robot manipulates the object without prehension. The robot infers the shape, motion and center of mass of the object based on the motion of the contact points as measured by tactile sensors. Our analysis is supported by simulation and experimental results.

Abstract While complex hands seem to offer generality, simple hands are more practical for most robotic and telerobotic manipulation tasks, and will remain so for the foreseeable future. This raises the question: how do generality and simplicity trade off in the design of robot hands? This paper explores the tension between simplicity in hand design and generality in hand function. It raises arguments both for and against simple hands; it considers several familiar examples; and it proposes a concept for a simple hand design with associated strategies for grasping and object localization. The central idea is to use knowledge of stable grasp poses as a cue for object localization. This leads to some novel design criteria, such as a desire to have only a few stable grasp poses. We explore some of the design implications for a binpicking task, and then examine some experimental results to see how this approach might be applied in an assistive object retrieval task. 1

this paper we present a model that integrates manipulation and tactile sensing. We derive equations for the shape and motion of an unknown object as a function of the motion of the manipulators and the sensor readings

We present a new method to reconstruct the shape of an unknown object using tactile sensors, without requiring object immobilization. Instead, sensing and nonprehensile manipulation occur simultaneously. The robot infers the shape, motion and center of mass of the object based on the motion of the contact points as measured by the tactile sensors. We present analytic results and simulation results assuming quasistatic dynamics. We prove that the shape and motion are observable in both the quasistatic and the fully dynamic case.

Abstract—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.