This paper addresses the design of a vision-based method to automatically deform soft objects into desired 2D shapes with robot manipulators. The method presents an innovative feedback representation of the object's shape (based on truncated Fourier series) and effectively exploits it to guide the soft object manipulation task. A new model calibration scheme that iteratively approximates a local deformation model from vision and motion sensory feedback is derived; this estimation method allows to manipulate objects with unknown deformation properties. Pseudocode algorithms are presented to facilitate the implementation of the controller. Numerical simulations and a experiments are reported to validate this new approach.

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In this paper, we address the active deformation control of compliant objects by robot manipulators. The control of deformations is needed to automate several important tasks, for example, the manipulation of soft tissues, shaping of food materials, or needle insertion. Note that in many of these applications, the object's deformation properties are not known. To cope with this issue, in this paper we present two new visual servoing approaches to explicitly servo-control elastic deformations. The novelty of our kinematic controllers lies in its uncalibrated behavior; our adaptive methods do not require the prior identification of the object's deformation model and the camera's intrinsic/extrinsic parameters. This feature provides a way to automatically control deformations in a model-free manner. The experimental results that we report validate the feasibility of our controllers.

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Deformable object manipulation (DOM) is an emerging research problem in robotics. The ability to manipulate deformable objects endows robots with higher autonomy and promises new applications in the industrial, services, and healthcare sectors. However, compared to rigid object manipulation, the manipulation of deformable objects is considerably more complex, and is still an open research problem. Tackling the challenges in DOM demands breakthroughs in almost all aspects of robotics, namely hardware design, sensing, deformation modeling, planning, and control. In this article, we highlight the main challenges that arise by considering deformation and review recent advances in each sub-field. A particular focus of our paper lies in the discussions of these challenges and proposing promising directions of research.

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The objective of this paper is to present a systematic review of existing sensor-based control methodologies for applications that involve direct interaction between humans and robots, in the form of either physical collaboration or safe coexistence. To this end, we first introduce the basic formulation of the sensor-servo problem, and then, present its most common approaches: vision-based, touch-based, audio-based, and distance-based control. Afterwards, we discuss and formalize the methods that integrate heterogeneous sensors at the control level. The surveyed body of literature is classified according to various factors such as: sensor type, sensor integration method, and application domain. Finally, we discuss open problems, potential applications, and future research directions.