GelSlim: A High-Resolution, Compact, Robust, and Calibrated Tactile-sensing Finger

This work describes the development of a high-resolution tactile-sensing finger for robot grasping. This finger, inspired by previous GelSight sensing techniques, features an integration that is slimmer, more robust, and with more homogeneous output than previous vision-based tactile sensors. To achieve a compact integration, we redesign the optical path from illumination source to camera by combining light guides and an arrangement of mirror reflections. We parameterize the optical path with geometric design variables and describe the tradeoffs between the finger thickness, the depth of field of the camera, and the size of the tactile sensing area. The sensor sustains the wear from continuous use -- and abuse -- in grasping tasks by combining tougher materials for the compliant soft gel, a textured fabric skin, a structurally rigid body, and a calibration process that maintains homogeneous illumination and contrast of the tactile images during use. Finally, we evaluate the sensor's durability along four metrics that track the signal quality during more than 3000 grasping experiments.Comment: RA-L Pre-print. 8 page

The Design, Construction, and Experimental Characterization of Spatial Parallel Architectures of Elastofluidic Systems

Creating organic, life like motion has historically been extremely difficult and costly for general applications. Traditional structures and robots use rigid components with discrete joints to produce desired motions but are limited in freedom by the range of motion each additional component allows. In a traditionally rigid robot complex motion is obtained through the addition of joints and links. These additions add complexity to a rigid robot but improve its ability to create motion. Soft robotics aims to overcome the limitations of traditional robotics by creating integrated actuation and structure which more closely imitates organic movement. Often the most effective examples to learn from are natural phenomenon or organisms such as underwater and land based invertebrates. In pursuit of the goal of effective soft robotics researchers have explored the idea of a pneumatic elastofluidic actuator, one which expands and deforms in response to applied pressure. While these systems have demonstrated some limited success, they are often used either as a single entity or in series with one another to produce novel motions. In this thesis parallel structures made of these actuators are shown to have the potential to be extremely powerful and useful for soft robotic applications. These spatial arrangements of connected and dependent actuators exhibit behaviors impossible for a single actuator. This research concerns the effective design and construction of these complex parallel structures in an attempt to define a method of characterization which produces useful and desirable spatial architectures and motions

Computer- and robot-assisted Medical Intervention

Medical robotics includes assistive devices used by the physician in order to make his/her diagnostic or therapeutic practices easier and more efficient. This chapter focuses on such systems. It introduces the general field of Computer-Assisted Medical Interventions, its aims, its different components and describes the place of robots in that context. The evolutions in terms of general design and control paradigms in the development of medical robots are presented and issues specific to that application domain are discussed. A view of existing systems, on-going developments and future trends is given. A case-study is detailed. Other types of robotic help in the medical environment (such as for assisting a handicapped person, for rehabilitation of a patient or for replacement of some damaged/suppressed limbs or organs) are out of the scope of this chapter.Comment: Handbook of Automation, Shimon Nof (Ed.) (2009) 000-00

Learning Deep NBNN Representations for Robust Place Categorization

This paper presents an approach for semantic place categorization using data obtained from RGB cameras. Previous studies on visual place recognition and classification have shown that, by considering features derived from pre-trained Convolutional Neural Networks (CNNs) in combination with part-based classification models, high recognition accuracy can be achieved, even in presence of occlusions and severe viewpoint changes. Inspired by these works, we propose to exploit local deep representations, representing images as set of regions applying a Na\"{i}ve Bayes Nearest Neighbor (NBNN) model for image classification. As opposed to previous methods where CNNs are merely used as feature extractors, our approach seamlessly integrates the NBNN model into a fully-convolutional neural network. Experimental results show that the proposed algorithm outperforms previous methods based on pre-trained CNN models and that, when employed in challenging robot place recognition tasks, it is robust to occlusions, environmental and sensor changes