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About

Riddhiman Laha is a robotics researcher whose work centers on motion planning, manipulation, and human-robot collaboration, with a particular focus on making robots smarter, safer, and more adaptable in dynamic real-world environments. His research spans dual-arm cooperative manipulation, reactive planning, and trajectory optimization, consistently bridging the gap between theoretical rigor and practical deployment. Among his most influential contributions is his work on multi-agent predictive planning for reactive dual-arm manipulation (25 citations), which advances how robots handle complex, fast-changing scenarios comparable to human dexterity. His elegant solution to slosh-free trajectory optimization (22 citations) addresses a long-standing challenge in fluid transportation by modeling end-effector dynamics as a spherical pendulum system, offering a computationally efficient and generalizable approach. His reactive cooperative manipulation framework using set primitives and circular fields (21 citations) further demonstrates his knack for elegant, real-time-capable solutions. Laha has also made notable strides in user-guided planning, shared autonomy teleoperation, and workspace capability analysis through Enhanced Dexterity Maps, reflecting a holistic vision of intuitive, human-centered robotics. With over 140 cumulative citations across a focused body of work, his research is establishing a strong foundation for the next generation of collaborative and autonomous robotic systems.