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Object pose estimation is a fundamental problem in robotics and computer vision, yet it remains challenging due to partial …

Contact-rich manipulation depends on applying the correct grasp forces throughout the manipulation task, especially when handling …

Contact-rich manipulation remains a major challenge in robotics. Optical tactile sensors like GelSight Mini offer a low-cost solution …

Machine learning methods have a groundbreaking impact in many application domains, but their application on real robotic platforms is …

Finalist for IROS Best Paper Award on Mobile Manipulation This work investigates the effectiveness of tactile sensing for the practical everyday problem of stable object placement on flat surfaces starting from unknown initial poses. We devise a neural architecture that estimates a rotation matrix, resulting in a corrective gripper movement that aligns the object with the placing surface for the subsequent object manipulation. We compare models with different sensing modalities, such as force-torque, an external motion capture system, and two classical baseline models in real-world object placing tasks with different objects. The experimental evaluation of our placing policies with a set of unseen everyday objects reveals significant generalization of our proposed pipeline, suggesting that tactile sensing plays a vital role in the intrinsic understanding of robotic dexterous object manipulation.

This work presents a training procedure and a pretrained model that can estimate the normal force distribution and the contact area acting upon the GelSight Mini visuotactile sensor. The representation maps from the raw output images of the sensor to the normal force distribution acting across the entire sensor surface. This is appealing as it allows to directly do classical force control based on the output of visuotactile sensors. Moreover, it yields a physically grounded, interpretable representation of the tactile signals.

Robot assembly discovery is a challenging problem that lives at the intersection of resource allocation and motion planning. The goal …

Best Paper Award in the Geometric Representations Workshop at ICRA 2023 We propose learning task-space, data-driven cost functions as diffusion models. Diffusion models represent expressive multimodal distributions and exhibit proper gradients over the entire space. We exploit these properties for motion optimization by integrating the learned cost functions with other costs in a single objective function, and optimize all of them jointly by gradient descent.

Spotlight Presentation and Poster Presentation! - Robot assembly discovery is a challenging problem that lives at the intersection of …

Assembly problems are demanding as they require abstract high-level reasoning over action sequences together with a smooth execution of …