SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation

Understand

Manipulating deformable objects has long been a challenge in robotics due to its high dimensional state representation and complex dynamics.

• Recent success in deep reinforcement learning provides a promising direction for learning to manipulate deformable objects with data driven methods.
• However, existing reinforcement learning benchmarks only cover tasks with direct state observability and simple low-dimensional dynamics or with relatively simple image-based environments, such as those with rigid objects.
• In this paper, we present SoftGym, a set of open-source simulated benchmarks for manipulating deformable objects, with a standard OpenAI Gym API and a Python interface for creating new environments.