6 accepted papers
Recent advances in task planning leverage Large Language Models (LLMs) to improve generalizability by combining such models with classical planning algorithms to address their inherent limitations in reasoning capabilities. However, these approaches face the challenge of dynamically capturing the in…
To perform versatile mobile manipulation tasks in human-centered environments, the ability to efficiently transfer learned skills, knowledge, and experiences from one robot to another or across different environments is critical. In this paper, we present MAkEable, a versatile uni- and multi-manual…
Task-oriented object grasping and rearrangement are key skills for robots, which have to perform versatile real-world manipulation tasks. However, they remain challenging due to partial observations of the objects and shape variations in categorical objects. In this paper, we present the Multi-featu…
For a robot to perform a grasping and manipulation task, it has to determine possible robot placements in the workspace, from which target objects or environmental elements relevant to the given task are reachable. This work presents a novel approach for finding placements for the mobile base of a h…
Robust vision-based grasping and manipulation of unknown objects in unstructured scenes requires the extraction of action candidates based on visual information while taking into account noise and occlusions in such scenes. We address this problem by combining the concept of affordances and <i>Bayes
In real world applications, robotic solutions remain impractical due to the challenges that arise in unknown and unstructured environments. To perform complex manipulation tasks in complex and cluttered situations, robots need to be able to identify the interaction possibilities with the scene, i.e.…