Best Paper Award in the RSS 2024 Workshop on Priors4Robots This paper introduces a novel policy class combining Flow Matching with SE(3) Invariant Transformers for efficient, equivariant, and expressive robot learning from demonstrations. We showcase the performance of the method across simulation and real robot environment considering low dimensional as well as high dimensional real-world observations.

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.

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.

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.

We propose a novel hybrid method for Robot Assembly discovery that is based on a combination of Mixed Integer Programming and a graph-based reinforcement learning agent.

We propose a novel method for learning to assemble arbitrary structures from scratch. The transformer-like graph-based neural network jointly decides which blocks to use and how to assemble the structure with the robot-in-the-loop.

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.