2021/08/03 by Yu Sun, Sun, Yu, Joe Falco +7 · 2 citations
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Reinforcement Learning in Robotics #Robot Manipulation and Learning #Robotic Path Planning Algorithms #Robotics (cs.RO) #cs.AI #cs.RO
paper · pdf · doi:10.48550/arxiv.2108.01483
Accepted for publication by IEEE Robotics and Automation Letters
openalex publication_date 2021/08/03 · openalex created_date 2021/08/16 · arxiv created 2021/12/09 · arxiv updated 2021/12/10 · openalex updated_date 2026/07/28
This paper discusses recent research progress in robotic grasping and manipulation in the light of the latest Robotic Grasping and Manipulation Competitions (RGMCs). We first provide an overview of past benchmarks and competitions related to the robotics manipulation field. Then, we discuss the methodology behind designing the manipulation tasks in RGMCs. We provide a detailed analysis of key challenges for each task and identify the most difficult aspects based on the competing teams' performance in recent years. We believe that such an analysis is insightful to determine the future research directions for the robotic manipulation domain.