Max Robotics — Global Service Robot Directory + US Certification

About

Ruiqi Ni is an emerging robotics researcher whose work sits at the intersection of physics-informed machine learning and robot motion planning. His research addresses one of robotics' most fundamental challenges: enabling robots to rapidly and reliably find collision-free paths in complex, real-world environments. Ni's most significant contribution is the development of physics-informed neural motion planning frameworks, most notably through his progressive learning approach (2023, 16 citations) and NTFields (2022), which eliminate the need for large expert datasets by embedding physical constraints directly into neural network training. His work on constrained motion planning extends these methods to kinematic constraint manifolds, tackling the particularly difficult zero-volume constraint problem encountered in manipulation and legged locomotion tasks. Beyond single-robot settings, Ni has advanced multi-robot systems through a robust ADMM-based trajectory optimization framework (2022, 15 citations) that enables decentralized, scalable coordination. His most recent work integrates neural mapping with motion planning in unknown environments, pushing toward fully autonomous robot intelligence. With over 40 cumulative citations across his still-early career, Ni represents a promising voice in data-efficient, physically grounded approaches to robot autonomy.