Building generic robotic manipulation systems often requires large amounts of real-world data, which can be dificult to collect. Synthetic data generation offers a promising alternative, but limiting the sim-to-real gap requires significant engineering efforts. To reduce this engineering effort, we investigate the use of pretrained text-to-image diffusion models for texturing synthetic images and compare this approach with using random textures, a common domain randomization technique in synthetic data generation. We focus on generating object-centric representations, such as keypoints and segmentation masks, which are important for robotic manipulation and require precise annotations. We evaluate the efficacy of the texturing methods by training models on the synthetic data and measuring their performance on real-world datasets for three object categories: shoes, T-shirts, and mugs. Surprisingly, we find that texturing using a diffusion model performs on par with random textures, despite generating seemingly more realistic images. Our results suggest that, for now, using diffusion models for texturing does not benefit synthetic data generation for robotics. The code, data and trained models are available at \url{https://github.com/tlpss/diffusing-synthetic-data.git}.

Robots that assist humans will need to interact with articulated objects such as cabinets or microwaves. Early work on creating systems for doing so used proprioceptive sensing to estimate joint mechanisms during contact. However, nowadays, almost all systems use only vision and no longer consider proprioceptive information during contact. We believe that proprioceptive information during contact is a valuable source of information and did not find clear motivation for not using it in the literature. Therefore, in this paper, we create a system that, starting from a given grasp, uses proprioceptive sensing to open cabinets with a position-controlled robot and a parallel gripper. We perform a qualitative evaluation of this system, where we find that slip between the gripper and handle limits the performance. Nonetheless, we find that the system already performs quite well. This poses the question: should we make more use of proprioceptive information during contact in articulated object manipulation systems, or is it not worth the added complexity, and can we manage with vision alone? We do not have an answer to this question, but we hope to spark some discussion on the matter. The codebase and videos of the system are available at https://tlpss.github.io/revisiting-proprioception-for-articulated-manipulation/.