Adaptive Human-Robot Interaction: Human Inspired Handovers and Robotic Failure Explanations. (An Intersection of Robotics and Machine Learning) by Dr. Parag Khanna

Venue: Bharti-501/MS Teams

Abstract: 
As robots become more advanced, they are expected to be increasingly present among humans, engaging frequently in physical and social interactions. Among these interactions, handovers—the transfer of an object from one individual to another—play a vital role in daily life. This talk focuses on my research on enhancing human-robot interaction (HRI) by drawing inspiration from human-human handovers and utilizing handovers to resolve robotic failures by providing explanations for these failures as well as adapting these explanations based on human behavioral responses.

For physical interaction, I present my work on formulating a weight-adaptive robot grip release strategy that determines when to release an object as a human recipient begins to take it and adapts to variations in object weight. I recorded and published datasets of human-human handovers to develop data-driven (LSTM, VAE-LSTM based) grip release strategies, which were experimentally validated in user studies. I also present how object weight affects human motion during handovers, enabling robots to observe changes in human motion to estimate object weights and adapt their motions to convey weight information. Lastly, I present my research on the use of non-touch modalities, such as EEG brain signals and gaze tracking, to discern human intentions during HRI, differentiating between motions intended for handovers and those that are not.

For social interaction, I explored how different levels of explanation content impact collaborative performance of human-robot teams and human satisfaction. I present my research on explanation variation strategies for repeated failures and adapting explanations by predicting user confusion. I further present a context-specific explanation generation system using behavior tree representation of collaborative tasks combined with Large Language Models (LLMs). This system was implemented as a failure communication module that enabled adapting explanation levels based on user queries and behavior, effectively improving failure resolution rates for collaborative tasks, as evaluated in user studies.

By this talk, I aim to demonstrate how human-inspired approaches and machine learning can enhance both physical and social aspects of HRI, and to outline my future research directions for adaptive HRI.

Biosketch:
Dr. Parag Khanna is a postdoctoral researcher at the Division of Robotics, Perception, and Learning (RPL) at KTH Royal Institute of Technology, Sweden. As a collaborative roboticist, his research combines insights from human behavior, cognitive science, and robotics to design intuitive and explainable robotic systems. His key research topics include physical and social human-robot interaction (HRI), analyzing and learning from human behavior, data-driven and human-inspired robotic strategies, and adaptive explanations for robotic failures. He is passionate about bringing robotics from the lab to everyday life through adaptive, user-centered solutions that make robots more effective, safer, and easier to work with in real-world environments.

He received his PhD from KTH in 2025, focusing on human-robot interaction—specifically, developing adaptive techniques for seamless handovers between robots and humans. He holds dual M.Sc. degrees from the Erasmus Mundus European Masters in Advanced Robotics (EMARO+) program— from École Centrale de Nantes, France, and from the University of Genoa, Italy (2019). He earned his B.Tech. in Mechanical Engineering from Visvesvaraya National Institute of Technology (VNIT), Nagpur, India, in 2017, where his bachelor’s thesis on an autonomous snake robot reconfigurable into a quadcopter led to an Indian patent filed in 2017 (granted in 2025).

From 2019 to 2021, he worked as a research engineer at the French National Center for Scientific Research (CNRS) in Nantes, France, designing and controlling a bio-inspired tensegrity manipulator.

He has also organized workshops at the IEEE Humanoids conferences (2024–25) and the IEEE IROS 2025 conference, and serves as a program chair for the HRI Pioneers Workshop at the HRI 2026 conference and as a Associate Editor for the IEEE/SICE System Integration (SII) 2026 conference.

Dr. Khanna’s accomplishments include the Honorable Mention for Best Short Contribution Paper Award and selection as a HRI Pioneer at the ACM/IEEE HRI conference 2025, Best Poster Awards at KTH EECS Research and Impact Day 2025 and the Human Agent Interaction conference (HAI) 2023, and representation of VNIT at the National Innovation Club member meeting at the Rashtrapati Bhavan in 2017.