We propose to exploit in-hand tactile sensors for learning stable object placing on flat surfaces starting from unknown initial poses. Common approaches for object placing either require complete scene specifications or indirect sensor measurements, such as cameras which are prone to suffer from occlusions. Instead, this work proposes a novel approach for stable object placing that combines tactile feedback and proprioceptive sensing. Our experimental evaluation of the placing policies with a set of unknown everyday objects reveals an impressive generalization of the tactile-based pipeline and suggests that tactile sensing plays a vital role in the intrinsic understanding of dexterous object manipulation.

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.