Suveer Garg

University of Pennsylvania, Samsung (United States)

Papers

Total Citations

H-Index

About

Suveer Garg is a robotics researcher whose work sits at the intersection of healthcare technology and autonomous manipulation. He is best known for advancing social robotics in clinical settings and developing novel motion planning algorithms for complex environments. His highly cited paper on a social robot–augmented telehealth platform (19 citations) demonstrated how robotic systems can address critical shortages in rehabilitation access, particularly for stroke and cerebral palsy patients in rural areas during the COVID-19 pandemic. In motion planning, Garg introduced RAMP (15 citations), a hierarchical reactive planner that fuses sampling-based methods with Model Predictive Path Integral control to generate robust, real-time trajectories for manipulation tasks. Most recently, his work on HIO-SDF (9 citations) tackles the challenge of building space-efficient, incrementally updatable signed distance field representations for mobile robots exploring unknown environments. Garg’s contributions bridge practical healthcare needs with fundamental robotics challenges, making his research impactful for both rehabilitation engineering and autonomous systems.