ReForm: A Robot Learning Sandbox for Deformable Linear Object Manipulation
Paper in proceeding, 2021

Recent advances in machine learning have triggered an enormous interest in using learning-based approaches for robot control and object manipulation. While the majority of existing algorithms are evaluated under the assumption that the involved bodies are rigid, a large number of practical applications contain deformable objects. In this work we focus on Deformable Linear Objects (DLOs) which can be used to model cables, tubes or wires. They are present in many applications such as manufacturing, agriculture and medicine. New methods in robotic manipulation research are often demonstrated in custom environments impeding reproducibility and comparisons of algorithms. We introduce ReForm, a simulation sandbox and a tool for benchmarking manipulation of DLOs. We offer six distinct environments representing important characteristics of deformable objects such as elasticity, plasticity or self-collisions and occlusions. A modular framework is used, enabling design parameters such as the end-effector degrees of freedom, reward function and type of observation. ReForm is a novel robot learning sandbox with which we intend to facilitate testing and reproducibility in manipulation research for DLOs.

Robot control


Benchmark testing

Rendering (computer graphics)



Rita Laezza

Chalmers, Electrical Engineering, Systems and control

Robert Gieselmann

Royal Institute of Technology (KTH)

Florian T. Pokorny

Royal Institute of Technology (KTH)

Yiannis Karayiannidis

Chalmers, Electrical Engineering, Systems and control

Proceedings - IEEE International Conference on Robotics and Automation

10504729 (ISSN)

Vol. 2021-May 4717-4723
978-1-7281-9077-8 (ISBN)

2021 IEEE International Conference on Robotics and Automation
Xi'an, China,


C3SE (Chalmers Centre for Computational Science and Engineering)

Subject Categories


Control Engineering

Computer Science





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4/5/2022 6