Max Robotics — Global Service Robot Directory + US Certification

About

Aditya Agarwal is a roboticist pushing the boundaries of autonomous manipulation in unstructured, real-world environments. His research centers on motion planning, perception, and real-time control for mobile manipulation, with a particular focus on making robots capable of performing human-level tasks in settings where direct human presence is dangerous or impractical. Agarwal’s most cited work, "EDMP: Ensemble-of-costs-guided Diffusion for Motion Planning" (2024, 22 citations), introduces a novel diffusion-based framework that leverages an ensemble of cost functions to generate efficient motion plans, offering remarkable adaptability to new scenes without task-specific training. This contribution addresses a core challenge in robotic manipulation: balancing generality with computational efficiency. His earlier work on the RoMan platform (2020, 6 citations) demonstrated a tangible step toward fieldable, human-scale mobile manipulation in unstructured environments, while his provably constant-time planning algorithm (2020, 5 citations) enables reliable real-time grasping of objects off conveyor belts—a critical capability for industrial automation. Agarwal has also contributed to the perception challenges underlying table-top rearrangement (2022, 4 citations). Through these efforts, he is helping to bridge the gap between laboratory robotics and practical, deployable systems.