Generation of GelSight Tactile Images for Sim2Real Learning (2101.07169v1)

Abstract: Most current works in Sim2Real learning for robotic manipulation tasks leverage camera vision that may be significantly occluded by robot hands during the manipulation. Tactile sensing offers complementary information to vision and can compensate for the information loss caused by the occlusion. However, the use of tactile sensing is restricted in the Sim2Real research due to no simulated tactile sensors being available. To mitigate the gap, we introduce a novel approach for simulating a GelSight tactile sensor in the commonly used Gazebo simulator. Similar to the real GelSight sensor, the simulated sensor can produce high-resolution images by an optical sensor from the interaction between the touched object and an opaque soft membrane. It can indirectly sense forces, geometry, texture and other properties of the object and enables Sim2Real learning with tactile sensing. Preliminary experimental results have shown that the simulated sensor could generate realistic outputs similar to the ones captured by a real GelSight sensor. All the materials used in this paper are available at https://danfergo.github.io/gelsight-simulation.

• The paper introduces a simulation technique that replicates high-resolution tactile images using depth mapping and Gaussian filtering, achieving low error margins compared to real sensor outputs.
• It employs Phong shading to mimic complex light reflections, ensuring the simulated sensor adapts to varied illumination conditions and accurately represents tactile feedback.
• The research validates its approach with a CNN-based tactile shape classification task, achieving over 76% accuracy in Sim2Real transfer learning scenarios.

Generation of GelSight Tactile Images for Sim2Real Learning

The paper "Generation of GelSight Tactile Images for Sim2Real Learning" presents a novel methodology to simulate GelSight tactile sensors within the Gazebo simulator. This innovation addresses a significant barrier in the Sim2Real transfer learning domain, where tactile sensing has been underutilized due to the absence of realistic virtual tactile sensors. This work contributes to filling this gap by precisely modeling the GelSight sensor's tactile feedback, crucial for various robotic manipulation tasks.

Core Contributions

The authors propose a detailed method for simulating a GelSight sensor, capable of producing high-resolution tactile images analogous to those obtained from physical sensors. This simulation deviates from purely vision-based models that often fail in scenarios with occlusions or suboptimal lighting. Incorporating tactile sensing compensates for these deficiencies by providing critical data regarding the object's properties, such as texture and geometry, as identified through contact-based interactions.

1. Simulation Setup: The simulation mimics a GelSight sensor's operation by utilizing a depth camera in combination with Gaussian filtering techniques to emulate the elastomer surface deformation. The model captures the depth map of objects interacting with the simulated sensor. This depth data is then processed to estimate the tactile surface’s deformation, resembling the real-tactile sensation.
2. Rendering Tactile Feedback: The rendering process employs Phong shading, a classic model in computer graphics, to simulate internal light reflections and shading that occur within the real sensor. Adjustments in the Phong model parameters allow the simulation to replicate various real-world illumination conditions characteristic to different GelSight versions.
3. Experimental Verification: The paper presents an experimental setup where both real and simulated GelSight sensors collect tactile images of various 3D printed objects. The virtual dataset parallels the real one, enabling comprehensive evaluations of the simulated sensor outputs. Quantitative analysis using metrics like Mean Absolute Error (MAE) and Structural Similarity Index (SSIM) demonstrate remarkable fidelity of the simulated images with real tactile data, with notably low error margins.
4. Sim2Real Transfer Learning: The use of the developed simulation for training a neural network in a tactile shape classification task illustrates the efficacy of the approach in Sim2Real transfer learning contexts. A Convolutional Neural Network (CNN) trained with simulated images reached accuracy levels of over 76% on real-world data, benefiting significantly from texture augmentation as part of the training regimen.

Implications and Future Directions

The implications of this research are profound, particularly in enhancing robotic systems' tactile capabilities, a crucial facet for sophisticated interaction with environments and tasks requiring dexterous manipulation. Realistic tactile feedback simulations might extend this paradigm to various robotics applications, from industrial automation to domestic assistance, where interaction nuances pose a challenge.

Theoretically, this work opens new avenues for studying tactile perception and learning, allowing researchers to explore interactions beyond the constraints of real sensor availability and potential wear in high-experimentation setups. Practically, the introduction of virtual tactile sensing into existing simulations encourages leveraging broader datasets for deep learning models, vastly improving generalization once deployed in real scenarios.

Future work could expand on optimizing the fidelity of the simulation under varying conditions and sensor configurations, potentially incorporating machine learning models directly into the rendering process to enhance realism and adaptability. Additionally, transferring this GelSight model into different simulation platforms like Unity or PyBullet could broaden its accessibility and applicability, fostering cross-disciplinary research in tactile robotics.

In conclusion, the development discussed in this paper represents a crucial step towards robust Sim2Real learning frameworks within tactile robotics, showing significant promise in both augmentation of robot capability and expansion of research possibilities.