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Nitish V. Thakor

5 accepted papers

2025

Human-Inspired Soft Anthropomorphic Hand System for Neuromorphic Object and Pose Recognition Using Multimodal Signals

IROS 2025

The human somatosensory system integrates multimodal sensory feedback, including tactile, proprioceptive, and thermal signals, to enable comprehensive perception and effective interaction with the environment. Inspired by the biological mechanism, we present a sensorized soft anthropomorphic hand eq

Cited by 0SourceScholar
2025

Weight Regression for a Generalized Motion Primitive Formulation in Cooperative Hand Placement Tasks with Upper-Limb Prostheses

IROS 2025

Recent years have seen a growing interest in the development of shared control strategies for upper limb prostheses. In this work, we take a critical step towards developing transhumeral devices by proposing a biomimetic control strategy for cooperative hand placement. This is achieved through a nov

Cited by 0SourceScholar
2023

A Bio-Plausible Approach to Realizing Heat-Evoked Nociceptive Withdrawal Reflex on the Upper Limb of a Humanoid Robot

RA-L 2023

In this letter, we present a method for realizing the heat-evoked nociceptive withdrawal reflex (NWR) in the upper limb of a humanoid robot so that it can avoid the potential damage caused by noxious heat. We use a spiking neural network whose structure, encoding scheme, and form of information tran

Cited by 4SourceScholar
2017

Hybrid Tele-Manipulation System Using a Sensorized 3-D-Printed Soft Robotic Gripper and a Soft Fabric-Based Haptic Glove

RA-L 2017

This paper presents a hybrid tele-manipulation system, comprising of a sensorized 3-D-printed soft robotic gripper and a soft fabric-based haptic glove that aim at improving grasping manipulation and providing sensing feedback to the operators. The flexible 3-D-printed soft robotic gripper broadens

Cited by 86SourceScholar
2016

High Precision Neural Decoding of Complex Movement Trajectories Using Recursive Bayesian Estimation With Dynamic Movement Primitives

RA-L 2016

Brain-machine interfaces (BMIs) are a rapidly progressing technology with the potential to restore function to victims of severe paralysis via neural control of robotic systems. Great strides have been made in directly mapping a user's cortical activity to control of the individual degrees of freedo

Cited by 24SourceScholar