Reinforcement Learning (RL) can enable agents to learn complex tasks. However, it is difficult to interpret the knowledge and reuse it across tasks. Inductive biases can address such issues by explicitly providing generic yet useful decomposition that is otherwise difficult or expensive to learn implicitly. For example, object-centered approaches decompose a high dimensional observation into individual objects. Expanding on this, we utilize an inductive bias for explicit object-centered knowledge separation that provides further decomposition into semantic representations and dynamics knowledge. For this, we introduce a semantic module that predicts an objects' semantic state based on its context. The resulting affordance-like object state can then be used to enrich perceptual object representations. With a minimal setup and an environment that enables puzzle-like tasks, we demonstrate the feasibility and benefits of this approach. Specifically, we compare three different methods of integrating semantic representations into a model-based RL architecture. Our experiments show that the degree of explicitness in knowledge separation correlates with faster learning, better accuracy, better generalization, and better interpretability.

To allow for safe Human-Robot-Interaction in industrial scenarios like manufacturing plants, it is essential to always be aware of the location and pose of humans in the shared workspace. We introduce a real-time 3D pose estimation system using single depth images that is aimed to run on limited hardware, such as a mobile robot. For this, we optimized a CNN-based 2D pose estimation architecture to achieve high frame rates while simultaneously requiring fewer resources. Building upon this architecture, we extended the system for 3D estimation to directly predict Cartesian body joint coordinates. We evaluated our system on a newly created dataset by applying it to a specific industrial workbench scenario. The results show that our system's performance is competitive to the state of the art at more than five times the speed for single person pose estimation.

We present a cooperative reasoning agent embodied into a mobile robot enabled to explore its environment by a camera. The robot can infer missing knowledge given an action and an object by the user, like for example “I want to open a wine bottle.”. Available tools are explored by the robot and it finally recommends the most suitable one. The reasoning is on the one hand based on a static ontology that describes how to relate the actions “open” and “cut” with a fixed set of tools. On the other hand, unknown actions and tools are resolved by looking up for synonyms or super/sub-class relations in Wikidata and WordNet. By this, the robot tries to map knowledge from linked data to its internal interpretable ontology. To retrace the reasoning process, the robot is able to explain its conclusions by text to speech. Finally, we show the performance of the system based on different settings for scanning the linked data.