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This work proposes a new event-based optical tactile sensor called Evetac. The main motivation for investigating event-based optical tactile sensors is their high spatial and temporal resolutions and low data rates. Benchmarking experiments demonstrate Evetac’s capabilities of sensing vibrations up to 498 Hz, reconstructing shear forces, and significantly reducing data rates compared to RGB optical tactile sensors. Moreover, Evetac’s output provides meaningful features for learning data-driven slip detection and prediction models. The learned models form the basis for a robust and adaptive closed-loop grasp controller capable of handling a wide range of objects. We believe that fast and efficient event-based tactile sensors like Evetac will be essential for bringing human-like manipulation capabilities to robotics.

We present a benchmark of structured policies for real-world dexterous manipulation. Our proposed approaches combine elements of classical robotics and modern policy optimization. This inclusion of inductive biases facilitates sample efficiency, interpretability, reliability and high performance.

We present a framework for model-free learning of event-triggered control strategies. Event-triggered methods aim to achieve high …