Max Robotics — Global Service Robot Directory + US Certification

About

Mukesh Singhal is a leading researcher at the intersection of robotics, human-robot interaction, and machine learning, with a core focus on enabling autonomous systems to learn and adapt to human preferences. His most significant contributions lie in developing novel frameworks for reward learning, where robots infer human intentions not just from passive observation, but through active, hierarchical queries. Singhal pioneered methods that allow robots to learn desired objective functions by asking targeted comparison questions—such as which of two trajectories is preferable—and augmenting this with richer feature queries, making the learning process more efficient and less burdensome for human users. His work on trust dynamics in human-autonomous vehicle interaction has also been highly influential, providing foundational models for safe co-existence between human and robot drivers. With his most cited papers accumulating over 80 citations, Singhal’s research is shaping how robots can effectively and safely operate in diverse, human-centered environments. His 2019 work on active learning from hierarchical queries stands out for its practical approach to bridging the gap between complex human values and robotic decision-making, marking him as a key figure in the future of collaborative autonomy.