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Thanks to recent advancements in the development of inexpensive, high-resolution tactile sensors, touch sensing has become popular in contact-rich robotic manipulation tasks. With the surge of data-driven methods and their requirement for substantial datasets, several methods of simulating tactile sensors have emerged in the tactile research community to overcome real-world data collection limitations. These simulation approaches can be split into two main categories: fast but inaccurate (soft) point-contact models and slow but accurate finite element modeling. In this work, we present a novel approach to simulating pressure-based tactile sensors using the hydroelastic contact model, which provides a high degree of physical realism at a reasonable computational cost. This model produces smooth contact forces for soft-to-soft and soft-to-rigid contacts along even non-convex contact surfaces. Pressure values are approximated at each point of the contact surface and can be integrated to calculate sensor outputs. We validate our models' capacity to synthesize real-world tactile data by conducting zero-shot sim-to-real transfer of a model for object state estimation. Our simulation is available as a plug-in to our open-source, MuJoCo-based simulator.

Non-prehensile manipulation, such as pushing objects to a desired target position, is an important skill for robots to assist humans in everyday situations. However, the task is challenging due to the large variety of objects with different and sometimes unknown physical properties, such as shape, size, mass, and friction. This can lead to the object overshooting its target position, requiring fast corrective movements of the robot around the object, especially in cases where objects need to be precisely pushed. In this paper, we improve the state-of-the-art by introducing a new memory-based vision-proprioception RL model to push objects more precisely to target positions using fewer corrective movements.

Most commercially available haptic gloves compromise the accuracy of hand-posture measurements in favor of a simpler design with fewer sensors. While inaccurate posture data is often sufficient for the task at hand in biomedical settings such as VR-therapy-aided rehabilitation, measurements should be as precise as possible to digitally recreate hand postures as accurately as possible. With these applications in mind, we have added extra sensors to the commercially available Dexmo haptic glove by Dexta Robotics and applied kinematic models of the haptic glove and the user's hand to improve the accuracy of hand-posture measurements. In this work, we describe the augmentations and the kinematic modeling approach. Additionally, we present and discuss an evaluation of hand posture measurements as a proof of concept.