Soft Contact Simulation and Manipulation Learning of Deformable Objects with Vision-based Tactile Sensor

TL;DR

This paper introduces a novel deformable object manipulation method using vision-based tactile sensors and advanced simulation, achieving high success rates in real-world tasks through reinforcement learning and sim-to-real transfer.

Contribution

It presents a new simulation environment and a vision-based tactile sensing approach for deformable object manipulation, enabling effective sim-to-real transfer and robust task performance.

Findings

Achieved 90% success rate in real robot tasks

Demonstrated robustness with different deformable objects

Validated superior performance over existing methods

Abstract

Deformable object manipulation is a classical and challenging research area in robotics. Compared with rigid object manipulation, this problem is more complex due to the deformation properties including elastic, plastic, and elastoplastic deformation. In this paper, we describe a new deformable object manipulation method including soft contact simulation, manipulation learning, and sim-to-real transfer. We propose a novel approach utilizing Vision-Based Tactile Sensors (VBTSs) as the end-effector in simulation to produce observations like relative position, squeezed area, and object contour, which are transferable to real robots. For a more realistic contact simulation, a new simulation environment including elastic, plastic, and elastoplastic deformations is created. We utilize RL strategies to train agents in the simulation, and expert demonstrations are applied for challenging tasks. Finally, we build a real experimental platform to complete the sim-to-real transfer and achieve a 90% success rate on difficult tasks such as cylinder and sphere. To test the robustness of our method, we use plasticine of different hardness and sizes to repeat the tasks including cylinder and sphere. The experimental results show superior performances of deformable object manipulation with the proposed method.