A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …

Robotic technology has transformed manufacturing industry ever since the first industrial
robot was put in use in the beginning of the 60s. The challenge of developing flexible …

Recent approaches in robot perception follow the insight that perception is facilitated by
interaction with the environment. These approaches are subsumed under the term …

We propose a decentralized variant of Monte Carlo tree search (MCTS) that is suitable for a
variety of tasks in multi-robot active perception. Our algorithm allows each robot to optimize …

People often use physical intuition when manipulating articulated objects, irrespective of
object semantics. Motivated by this observation, we identify an important embodied task …

Despite major progress in Robotics and AI, robots are still basically “zombies” repeatedly
achieving actions and tasks without understanding what they are doing. Deep-Learning AI …

Learning to act in unstructured environments such as cluttered piles of objects poses a
substantial challenge for manipulation robots. We present a novel neural network-based …

We introduce a particle filter-based approach to representing and actively reducing
uncertainty over articulated motion models. The presented method provides a probabilistic …

In industrial assembly tasks, the in-hand pose of grasped objects needs to be known with
high precision for subsequent manipulation tasks such as insertion. This problem (in-hand …

We propose a self-organising map (SOM) algorithm as a solution to a new multi-goal path
planning problem for active perception and data collection tasks. We optimise paths for a …