index – iHuman Lab

Research @ iHuman Lab

Classification of Reaction Time in Simulated Multiple Robot Tele-Exploration

In the teleoperated scenario, as the task difficulty increases, the performance of the operator decreases which leads to a decrease in the overall system efficiency. Thus…

Hemanth Manjunatha

Extracting Interpretable EEG Features from a Deep Learning Model to Assess the Quality of Human-Robot Co-manipulation

Imagine a world where robots work hand-in-hand with humans, anticipating our needs and adapting to our mental state. This future is becoming increasingly real, with…

Hemanth Manjunatha

SHASTA A Simulator for Human and Swarm Team Applications

Human-Swarm Teams uniquely combine the ability of swarms and cognition of humans to achieve complex behaviors. There is a need for a human-in-the-loop simulation framework…

Hemanth Manjunatha

Selective Eye-gaze Augmentation To Enhance Imitation Learning In Atari Games

This paper presents the selective use of eye-gaze information in learning human actions in Atari games. Extensive evidence suggests that our eye movements convey a wealth of…

Hemanth Manjunatha

Task Difficulty Prediction in Physical Human Interaction using EEG and Deep Learning

An experiment was conducted in which the subjects have to guide an robot under admittance control through a predefined trajectory. The damping in the admittance control was…

Hemanth Manjunatha

Transfer Learning of Motor Difficulty Classification in Physical Human-Robot Interaction Using Electromyography

Efficient human-robot collaboration during physical interaction requires estimating the human state for optimal role allocation and load sharing. Machine learning (ML)…

Hemanth Manjunatha