Simulation Environment
Simulation environments are increasingly crucial for developing and testing AI agents and robotic systems across diverse domains, from autonomous vehicles and robotics to healthcare and manufacturing. Current research focuses on creating more realistic and controllable simulations, often leveraging large language models (LLMs) and advanced generative models to produce diverse scenarios and complex interactions, including multimodal data and human-in-the-loop components. These advancements enable more rigorous evaluation of AI algorithms, facilitate the development of robust and generalizable systems, and ultimately accelerate progress in various fields by providing safer, more efficient, and cost-effective testing grounds.
Papers
RoboGSim: A Real2Sim2Real Robotic Gaussian Splatting Simulator
Xinhai Li, Jialin Li, Ziheng Zhang, Rui Zhang, Fan Jia, Tiancai Wang, Haoqiang Fan, Kuo-Kun Tseng, Ruiping Wang
DrivingSphere: Building a High-fidelity 4D World for Closed-loop Simulation
Tianyi Yan, Dongming Wu, Wencheng Han, Junpeng Jiang, Xia Zhou, Kun Zhan, Cheng-zhong Xu, Jianbing Shen