The use of simulation in robotics is increasingly widespread for the purpose of testing, synthetic data generation and skill learning. A relevant aspect of simulation for a variety of robot applications is physics-based simulation of robot-object interactions. This involves the challenge of accurately modeling and implementing different mechanical systems such as rigid and deformable bodies as well as their interactions via constraints, contact or friction. Most state-of-the-art physics engines commonly used in robotics either cannot couple deformable and rigid bodies in the same framework, lack important systems such as cloth or shells, have stability issues in complex friction-dominated setups or cannot robustly prevent penetrations. In this paper, we propose a framework for strongly coupled simulation of rigid and deformable bodies with focus on usability, stability, robustness and easy access to state-of-the-art deformation and frictional contact models. Our system uses the Finite Element Method (FEM) to model deformable solids, the Incremental Potential Contact (IPC) approach for frictional contact and a robust second order optimizer to ensure stable and penetration-free solutions to tight tolerances. It is a general purpose framework, not tied to a particular use case such as grasping or learning, it is written in C++ and comes with a Python interface. We demonstrate our system’s ability to reproduce complex real-world experiments where a mobile vacuum robot interacts with a towel on different floor types and towel geometries. Our system is able to reproduce 100% of the qualitative outcomes observed in the laboratory environment. The simulation pipeline, named Stark (the German word for strong, as in strong coupling) is made open-source.

The correct classification of the surrounding terrain is an important ability of a mobile robot that drives in outdoor environments. Our robot uses a 3D LIDAR and a camera to classify terrain as either asphalt, cobblestones, grass, or gravel. We build on previous work where we modeled the terrain as a Conditional random field to account for spatial dependencies, which improved results substantially. We now show how to speed up the spatial classification by defining a new energy term for neighborhood relations. Moreover, we now also consider temporal dependencies as the robot moves. This not only further improves the results, but makes it possible to build local terrain maps of the environment. We describe how to efficiently integrate the classification results of each time step into the map in a probabilistic manner. By also detecting obstacles with the LIDAR, the robot can build combined terrain and elevation maps. We show that these maps can be used for semantic robot localization.

In the domain of agricultural robotics, one major application is crop scouting, e.g., for the task of weed control. For this task a key enabler is a robust detection and classification of the plant and species. Automatically distinguishing between plant species is a challenging task, because some species look very similar. It is also difficult to translate the symbolic high level description of the appearances and the differences between the plants used by humans, into a formal, computer understandable form. Also it is not possible to reliably detect structures, like leaves and branches in 3D data provided by our sensor. One approach to solve this problem is to learn how to classify the species by using a set of example plants and machine learning methods. In this paper we are introducing a method for distinguishing plant species using a 3D LIDAR sensor and supervised learning. For that we have developed a set of size and rotation invariant features and evaluated experimentally which are the most descriptive ones. Besides these features we have also compared different learning methods using the toolbox Weka. It turned out that the best methods for our application are simple logistic regression functions, support vector machines and neural networks. In our experiments we used six different plant species, typically available at common nurseries, and about 20 examples of each species. In the laboratory we were able to identify over 98% of these plants correctly.