Robot Simulation
Robot simulation is a crucial tool for developing and testing robotic algorithms and systems, aiming to bridge the gap between simulated and real-world performance. Current research emphasizes creating highly realistic and efficient simulators, often leveraging advanced rendering techniques like physically-based rendering and GPU parallelization, as well as incorporating large language models for automated task generation and data augmentation. These advancements are significantly impacting robotics research by enabling faster algorithm development, more robust sim-to-real transfer, and the creation of large, diverse datasets for training complex robotic behaviors, ultimately accelerating progress in areas like manipulation, navigation, and human-robot interaction.
Papers
GenSim: Generating Robotic Simulation Tasks via Large Language Models
Lirui Wang, Yiyang Ling, Zhecheng Yuan, Mohit Shridhar, Chen Bao, Yuzhe Qin, Bailin Wang, Huazhe Xu, Xiaolong Wang
Dynamic Manipulation of a Deformable Linear Object: Simulation and Learning
Qi Jing Chen, Timothy Bretl, Nghia Vuong, Quang-Cuong Pham
Using LOR Syringe Probes as a Method to Reduce Errors in Epidural Analgesia -- a Robotic Simulation Study
Nitsan Davidor, Yair Binyamin, Tamar Hayuni, Ilana Nisky
Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity
Jinghao Xin, Jinwoo Kim, Zhi Li, Ning Li