Sim-to-Real for Robotic Tactile Sensing via Physics-Based Simulation and Learned Latent Projections (2103.16747v1)

Abstract: Tactile sensing is critical for robotic grasping and manipulation of objects under visual occlusion. However, in contrast to simulations of robot arms and cameras, current simulations of tactile sensors have limited accuracy, speed, and utility. In this work, we develop an efficient 3D finite element method (FEM) model of the SynTouch BioTac sensor using an open-access, GPU-based robotics simulator. Our simulations closely reproduce results from an experimentally-validated model in an industry-standard, CPU-based simulator, but at 75x the speed. We then learn latent representations for simulated BioTac deformations and real-world electrical output through self-supervision, as well as projections between the latent spaces using a small supervised dataset. Using these learned latent projections, we accurately synthesize real-world BioTac electrical output and estimate contact patches, both for unseen contact interactions. This work contributes an efficient, freely-accessible FEM model of the BioTac and comprises one of the first efforts to combine self-supervision, cross-modal transfer, and sim-to-real transfer for tactile sensors.

• The paper introduces a GPU-accelerated finite element model that simulates the SynTouch BioTac sensor 75 times faster than CPU methods.
• It employs variational autoencoders in a self-supervised framework to map simulated tactile deformations to real-world electrical signals.
• The approach accurately estimates contact patches and outperforms fully supervised techniques across 2.6k unique interaction tests.

Sim-to-Real for Robotic Tactile Sensing via Physics-Based Simulation and Learned Latent Projections

The paper "Sim-to-Real for Robotic Tactile Sensing via Physics-Based Simulation and Learned Latent Projections" presents a robust framework for enhancing tactile sensing in robotics, focusing on the SynTouch BioTac sensor. This paper successfully bridges the simulation-to-reality gap by integrating physics-based simulations and advancements in self-supervised learning to effectively project simulated tactile data into real-world applications. The central contributions of this research are significantly relevant for the progression of tactile sensing technology.

Primary Contributions

The research introduces a sophisticated 3D finite element method (FEM) model of the SynTouch BioTac sensor developed using NVIDIA's GPU-based Isaac Gym simulator. The simulations performed closely match the results of an industry-standard CPU-based FEM model validated against experimental data. Notably, the GPU-based simulations operate at an impressive speed that is approximately 75 times faster than its CPU counterparts, fostering real-time application potential.

The authors utilize self-supervised learning techniques to map simulated BioTac deformation data to real-world electrical outputs. By generating and coupling latent representations of deformation fields and electrical signals through variational autoencoders (VAE), they facilitate accurate cross-modal projections. This dual-layer learning approach reveals the practical application of sim-to-real transfer in tactile sensors, demonstrating the tangible potential of this innovative methodology.

Numerical Results and Validation

Through rigorous simulation and experimental procedures, the authors validate the viability of their approach by accurately synthesizing real-world BioTac electrical data and estimating contact patches for unseen contact interactions. The mean $\ell^2$-norm of the force error vector, optimally tuned and evaluated using a sequence of diverse indentations across different objects, denotes that the BioTac model from Isaac Gym can effectively generalize beyond the specific instances on which it was trained.

Additionally, the paper employs a robust dataset encompassing 2.6k unique interactions, further reinforcing the generalizability of the proposed method. The empirical analysis indicates that the method proposed, referred to as Latent Projection (LP), outperforms a Fully Supervised technique in terms of predictive accuracy and error reduction across multiple scenarios.

Implications and Future Directions

The implications of this paper extend to both theoretical and practical aspects of robotic tactile sensing. The achieved advance in sim-to-real transfer paves the way for more sophisticated feedback systems in robotic manipulation, potentially revolutionizing haptic technology in automation and enabling safer, more nuanced interaction with objects in occluded environments or delicate contexts.

Looking forward, this framework lends itself to adaptation and further exploration across different tactile sensors beyond the SynTouch BioTac. Enhancements in FEM modeling could inculcate more complex sensor dynamics and behaviors, potentially integrating dynamic control in simulations. The comprehensive self-supervision approach introduced across distinct sensing platforms also opens new opportunities for cross-sensory learning algorithms.

This paper establishes a significant milestone in the domain of robotic tactile sensing, marking the synthesis of simulation precision and cross-modal representation learning as pivotal strategies to spearhead future tactile sensor advancements. The continued research may contribute to more efficient adaptability of robots into diverse real-world settings, promoting increased autonomy and reliability in robotic systems.