Research Challenges and Progress in Robotic Grasping and Manipulation Competitions
Yu Sun, Joe Falco, Maximo A. Roa, and Berk Calli

TL;DR
This paper reviews recent progress and challenges in robotic grasping and manipulation, focusing on insights gained from the Robotic Grasping and Manipulation Competitions to guide future research directions.
Contribution
It provides a comprehensive overview of past benchmarks, analyzes key challenges, and offers insights into future research directions in robotic manipulation based on competition performances.
Findings
Identification of key challenges in robotic manipulation tasks
Analysis of performance trends in recent RGMCs
Insights into future research directions for robotic grasping
Abstract
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.
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Taxonomy
TopicsRobot Manipulation and Learning · Reinforcement Learning in Robotics · Robotic Path Planning Algorithms
