Learning to bag with a simulation-free reinforcement learning framework for robots
This addresses the challenge of robot manipulation of deformable objects, which is incremental as it applies existing RL concepts to a specific domain without a new paradigm.
The paper tackles the problem of enabling robots to manipulate deformable objects like bags by presenting a simulation-free reinforcement learning framework that learns optimal grasping points, achieving success rates of 60% and 80% after three hours of training from folded and unfolded states, respectively.
Bagging is an essential skill that humans perform in their daily activities. However, deformable objects, such as bags, are complex for robots to manipulate. This paper presents an efficient learning-based framework that enables robots to learn bagging. The novelty of this framework is its ability to perform bagging without relying on simulations. The learning process is accomplished through a reinforcement learning algorithm introduced in this work, designed to find the best grasping points of the bag based on a set of compact state representations. The framework utilizes a set of primitive actions and represents the task in five states. In our experiments, the framework reaches a 60 % and 80 % of success rate after around three hours of training in the real world when starting the bagging task from folded and unfolded, respectively. Finally, we test the trained model with two more bags of different sizes to evaluate its generalizability.