Nikhil Deshpande

Sunny Katyara, Nikhil Deshpande, Fanny Ficuciello et al.

Handling non-rigid objects using robot hands necessities a framework that does not only incorporate human-level dexterity and cognition but also the multi-sensory information and system dynamics for robust and fine interactions. In this research, our previously developed kernelized synergies framework, inspired from human behaviour on reusing same subspace for grasping and manipulation, is augmented with visuo-tactile perception for autonomous and flexible adaptation to unknown objects. To detect objects and estimate their poses, a simplified visual pipeline using RANSAC algorithm with Euclidean clustering and SVM classifier is exploited. To modulate interaction efforts while grasping and manipulating non-rigid objects, the tactile feedback using T40S shokac chip sensor, generating 3D force information, is incorporated. Moreover, different kernel functions are examined in the kernelized synergies framework, to evaluate its performance and potential against task reproducibility, execution, generalization and synergistic re-usability. Experiments performed with robot arm-hand system validates the capability and usability of upgraded framework on stably grasping and dexterously manipulating the non-rigid objects.

Sunny Katyara, Nikhil Deshpande, Fanny Ficuciello et al.

Enabling robots to work in close proximity to humans necessitates a control framework that does not only incorporate multi-sensory information for autonomous and coordinated interactions but also has perceptive task planning to ensure an adaptable and flexible collaborative behaviour. In this research, an intuitive stack-of-tasks (iSoT) formulation is proposed, that defines the robot's actions by considering the human-arm postures and the task progression. The framework is augmented with visuo-tactile information to effectively perceive the collaborative environment and intuitively switch between the planned sub-tasks. The visual feedback from depth cameras monitors and estimates the objects' poses and human-arm postures, while the tactile data provides the exploration skills to detect and maintain the desired contacts to avoid object slippage. To evaluate the performance, effectiveness and usability of the proposed framework, assembly and disassembly tasks, performed by the human-human and human-robot partners, are considered and analyzed using distinct evaluation metrics i.e, approach adaptation, grasp correction, task coordination latency, cumulative posture deviation, and task repeatability.

Alperen Acemoglu, Nikhil Deshpande, Leonardo S. Mattos

This article presents the design and assembly of a novel magnetically actuated endoscopic laser scanner device. The device is designed to perform 2D position control and high speed scanning of a fiber-based laser for operation in narrow workspaces. The device includes laser focusing optics to allow non-contact incisions and tablet-based control interface for intuitive teleoperation. The performance of the proof-of-concept device is analysed through controllability and the usability studies. The computer-controlled high-speed scanning demonstrates repeatable results with 21 um precision and a stable response up to 48 Hz. Teleoperation user trials, were performed for trajectory-following tasks with 12 subjects, show an accuracy of 39 um. The innovative design of the device can be applied to both surgical and diagnostic (imaging) applications in endoscopic systems.