TY - JOUR
T1 - Review of Learning-Based Robotic Manipulation in Cluttered Environments
AU - Mohammed, Marwan Qaid
AU - Kwek, Lee Chung
AU - Chua, Shing Chyi
AU - Al-Dhaqm, Arafat
AU - Nahavandi, Saeid
AU - Eisa, Taiseer Abdalla Elfadil
AU - Miskon, Muhammad Fahmi
AU - Al-Mhiqani, Mohammed Nasser
AU - Ali, Abdulalem
AU - Abaker, Mohammed
AU - Alandoli, Esmail Ali
PY - 2022/10/18
Y1 - 2022/10/18
N2 - Robotic manipulation refers to how robots intelligently interact with the objects in their surroundings, such as grasping and carrying an object from one place to another. Dexterous manipulating skills enable robots to assist humans in accomplishing various tasks that might be too dangerous or difficult to do. This requires robots to intelligently plan and control the actions of their hands and arms. Object manipulation is a vital skill in several robotic tasks. However, it poses a challenge to robotics. The motivation behind this review paper is to review and analyze the most relevant studies on learning-based object manipulation in clutter. Unlike other reviews, this review paper provides valuable insights into the manipulation of objects using deep reinforcement learning (deep RL) in dense clutter. Various studies are examined by surveying existing literature and investigating various aspects, namely, the intended applications, the techniques applied, the challenges faced by researchers, and the recommendations adopted to overcome these obstacles. In this review, we divide deep RL-based robotic manipulation tasks in cluttered environments into three categories, namely, object removal, assembly and rearrangement, and object retrieval and singulation tasks. We then discuss the challenges and potential prospects of object manipulation in clutter. The findings of this review are intended to assist in establishing important guidelines and directions for academics and researchers in the future.
AB - Robotic manipulation refers to how robots intelligently interact with the objects in their surroundings, such as grasping and carrying an object from one place to another. Dexterous manipulating skills enable robots to assist humans in accomplishing various tasks that might be too dangerous or difficult to do. This requires robots to intelligently plan and control the actions of their hands and arms. Object manipulation is a vital skill in several robotic tasks. However, it poses a challenge to robotics. The motivation behind this review paper is to review and analyze the most relevant studies on learning-based object manipulation in clutter. Unlike other reviews, this review paper provides valuable insights into the manipulation of objects using deep reinforcement learning (deep RL) in dense clutter. Various studies are examined by surveying existing literature and investigating various aspects, namely, the intended applications, the techniques applied, the challenges faced by researchers, and the recommendations adopted to overcome these obstacles. In this review, we divide deep RL-based robotic manipulation tasks in cluttered environments into three categories, namely, object removal, assembly and rearrangement, and object retrieval and singulation tasks. We then discuss the challenges and potential prospects of object manipulation in clutter. The findings of this review are intended to assist in establishing important guidelines and directions for academics and researchers in the future.
KW - cluttered environment
KW - deep reinforcement learning
KW - dense clutter
KW - object grasping
KW - object manipulation
KW - robotic manipulation
KW - robotics
KW - sensory data
UR - http://www.scopus.com/inward/record.url?scp=85140932613&partnerID=8YFLogxK
U2 - 10.3390/s22207938
DO - 10.3390/s22207938
M3 - Review article
C2 - 36298284
AN - SCOPUS:85140932613
VL - 22
JO - Sensors
JF - Sensors
SN - 1424-3210
IS - 20
M1 - 7938
ER -