6 accepted papers
General-purpose robotic skills from end-to-end demonstrations often leads to task-specific policies that fail to generalize beyond the training distribution. Therefore, we introduce FunCanon, a framework that converts long-horizon manipulation tasks into sequences of action chunks, each defined by a…
Mobile manipulation requires the coordinated control of a mobile base and a robotic arm while simultaneously perceiving both global scene context and fine-grained object details. Existing single-view approaches often fail in unstructured environments due to limited fields of view, exploration, and g…
We introduce a scalable approach for object pose estimation trained on simulated RGB views of multiple 3D models together. We learn an encoding of object views that does not only describe an implicit orientation of all objects seen during training, but can also relate views of untrained objects. Our…
One of the main challenges in sampling-based motion planners is to find an efficient sampling strategy. While methods such as Rapidly-exploring Random Tree (RRT) have shown to be more reliable in complex environments than optimization-based methods, they often require longer planning times, which re…
We propose a real-time RGB-based pipeline for object detection and 6D pose estimation. Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views of a 3D model using Domain Randomization. This so-called Augmented Autoencoder has several…
In this paper, we address the problem of learning to classify new object classes and instances by adapting a previously trained classifier. The main challenges here are the small amount of newly available training data and the large change in appearance between the new and the old data. To address t