Gyanig Kumar

About

Gyanig Kumar is a rising researcher at the forefront of human-robot interaction (HRI) and extended reality (XR), specializing in making collaborative robots more intuitive and responsive. His work centers on intent prediction—decoding human hand motions and goals to enable seamless, rapid human-robot collaboration. Kumar’s key contributions lie in applying Deep Inverse Reinforcement Learning (IRL) to model and forecast human intentions during critical tasks like object handovers and rapid aiming movements. His 2024 papers, each garnering 7 citations, demonstrate a novel system that reduces the cognitive and physical burden on users by improving prediction accuracy in shared autonomy settings. By integrating these models into XR environments, Kumar bridges the gap between virtual interfaces and physical robot control, with applications spanning UI/UX design, automotive driver intent anticipation, and industrial HRI. His 2025 work further explores the dynamics of pre-programmed, high-speed motions, pushing toward real-time target forecasting. Through this innovative fusion of machine learning and ergonomic design, Kumar is laying the groundwork for a future where robots can anticipate our every move, making collaboration safer, faster, and more natural.

Research Focus

Key Achievements

Top Papers

1. 1
   Multimodal Target Prediction for Rapid Human-Robot Interaction

   7 citations · 2024
2. 2
   Enhanced Human-Robot Collaboration with Intent Prediction using Deep Inverse Reinforcement Learning

   7 citations · 2024
3. 3
   Investigating Inverse Reinforcement Learning during Rapid Aiming Movement in Extended Reality and Human-Robot Interaction

   2 citations · 2025

Key Collaborators

Contact & Links