Learning to Manipulate Deformable Objects without Demonstrations

Understand

In this paper we tackle the problem of deformable object manipulation through model-free visual reinforcement learning (RL).

• In order to circumvent the sample inefficiency of RL, we propose two key ideas that accelerate learning.
• First, we propose an iterative pick-place action space that encodes the conditional relationship between picking and placing on deformable objects.
• The explicit structural encoding enables faster learning under complex object dynamics.