Nikhil Somani

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In this paper, a scene perception and recognition module aimed at use in typical industrial scenarios is presented. The major contribution of this work lies in a 3D object detection, recognition and pose estimation module, which can be trained using CAD models and works for noisy data, partial views and in cluttered scenes. This algorithm was qualitatively(More)
Intuitive programming of industrial robots is especially important for small and medium-sized enterprises. We evaluated four different input modalities (touch, gesture, speech, 3D tracking device) regarding their preference, usability, and intuitiveness for robot programming. A Wizard-of-Oz experiment was conducted with 30 participants and its results show(More)
In this paper, we propose a framework for intuitive task-based programming of robots using geometric inter-relational constraints. The intended applications of this framework are robot programming interfaces that use semantically rich task descriptions, allow intuitive (re-)programming, and are suitable for non-expert users typically found in SMEs. A key(More)
In this paper, we introduce an approach for leveraging CAD description to a semantic level, in order to link additional knowledge to CAD models and to exploit resulting synergy effects. This has been achieved by designing a description language, based on the Web Ontology Language (OWL), that is used to define boundary representations (BREP) of objects. This(More)
This paper introduces a new comprehensive solution for the open problem of uncalibrated 3D image-based stereo visual servoing for robot manipulators. One of the main contributions of this article is a novel 3D stereo camera model to map positions in the task space to positions in a new 3D Visual Cartesian Space (a visual feature space where 3D positions are(More)
In this paper, an approach for matching of primitive shapes detected from point clouds, to boundary representations of primitive shapes contained in CAD models of objects/workpieces is presented. The primary target application is object detection and pose estimation from noisy RGBD sensor data. This approach can also be used to determine incomplete object(More)
We present a novel robot programming methodology that is aimed at reducing the level of robotics expert knowledge needed to operate industrial robotic systems by explicitly modeling this knowledge and abstracting it from the user. Most of the current robot programming paradigms are either user-centric and fully-specify the robot’s task to the lowest detail(More)
In this paper, an object recognition and pose estimation approach based on constraints from primitive shape matching is presented. Additionally, an approach for primitive shape detection from point clouds using an energy minimization formulation is presented. Each primitive shape in an object adds geometric constraints on the object’s pose. An algorithm is(More)
In this paper, we propose a framework for prioritized constraint-based specification of robot tasks. This framework is integrated with a cognitive robotic system based on semantic models of processes, objects, and workcells. The target is to enable intuitive (re-)programming of robot tasks, in a way that is suitable for non-expert users typically found in(More)
To synthesize whole-body behaviors interactively, multiple tasks and constraints need to be simultaneously satisfied, including those that guarantee the constraints imposed by the robot's structure and the external environment. In this paper, we present a prioritized, multiple-task control framework that is able to control forces in systems ranging from(More)