TacSL: A Library for Visuotactile Sensor Simulation and Learning

摘要

For both humans and robots, the sense of touch, known as tactile sensing, is critical for performing contact-rich manipulation tasks. Three key challenges in robotic tactile sensing are interpreting sensor signals, generating sensor signals in novel scenarios, and learning sensor-based policies. For visuotactile sensors, interpretation has been facilitated by their close relationship with vision sensors (e.g., RGB cameras). However, generation is still difficult, as visuotactile sensors typically involve contact, deformation, illumination, and imaging, all of which are expensive to simulate; in turn, policy learning has been challenging, as simulation cannot be leveraged for large-scale data collection. We present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TacSL</i> (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">taxel</i>), a library for GPU-based visuotactile sensor simulation and learning. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TacSL</i> can be used to simulate visuotactile images and extract contact-force distributions over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$200\times$</tex-math></inline-formula> faster than the prior state-of-the-art, all within the widely used Isaac simulator. Furthermore, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TacSL</i> provides a learning toolkit containing multiple sensor models, contact-intensive training environments, and online/offline algorithms that can facilitate policy learning for sim-to-real applications. On the algorithmic side, we introduce a novel online reinforcement-learning algorithm called asymmetric actor-critic distillation, designed to effectively and efficiently learn tactile-based policies in simulation that can transfer to the real world. Finally, we demonstrate the utility of our library and algorithms by evaluating the benefits of distillation and multimodal sensing for contact-rich manipulation tasks, and most critically, performing sim-to-real transfer.