Simon See

About

Simon See is a leading researcher at the frontier of robotics, artificial intelligence, and cyber-physical systems (CPS). His work centers on creating intelligent, resilient robotic systems that bridge the digital and physical worlds. A major contribution is his pioneering use of NVIDIA Isaac Sim to develop and benchmark AI-driven CPS for industrial robotics manipulation, with his 2024 paper on this topic already garnering 33 citations. See is also a key figure in advancing fault-tolerant locomotion for quadrupedal robots. Through reinforcement learning, he has developed methods that enable robots to maintain stable movement even after critical hardware failures, as detailed in his 2024 work on fault-tolerant quadruped locomotion (9 citations). Beyond physical robotics, See applies machine learning to solve complex mathematical problems, including a notable 2021 review on using ML for partial differential equations (12 citations). His work on 3D multi-object tracking, exemplified by the TrajectoryFormer (12 citations), is vital for autonomous driving and service robots. With a growing citation impact and a focus on practical, resilient AI, Simon See is shaping the future of autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Towards Building AI-CPS with NVIDIA Isaac Sim: An Industrial Benchmark and Case Study for Robotics Manipulation

   33 citations · 2024
2. 2
   Recent advance in machine learning for partial differential equation

   12 citations · 2021
3. 3
   TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses

   12 citations · 2023
4. 4
   Towards Fault-tolerant Quadruped Locomotion with Reinforcement Learning

   9 citations · 2024
5. 5
   Saving the Limping: Fault-tolerant Quadruped Locomotion via Reinforcement Learning

   3 citations · 2022
6. 6
   Towards Building AI-CPS with NVIDIA Isaac Sim: An Industrial Benchmark and Case Study for Robotics Manipulation

   3 citations · 2023

Key Collaborators

Contact & Links