Intelligent flat-and-textureless object manipulation in Service Robots

This work introduces our approach to the flat and textureless object grasping problem. In particular, we address the tableware and cutlery manipulation problem where a service robot has to clean up a table. Our solution integrates colour and 2D and 3D geometry information to describe objects, and this information is given to the robot action planner to find the best grasping trajectory depending on the object class. Furthermore, we use visual feedback as a verification step to determine if the grasping process has successfully occurred. We evaluate our approach in both an open and a standard service robot platform following the RoboCup@Home international tournament regulations.

Marimba: A Tool for Verifying Properties of Hidden Markov Models

The formal verification of properties of Hidden Markov Models (HMMs) is highly desirable for gaining confidence in the correctness of the model and the corresponding system. A significant step towards HMM verification was the development by Zhang et al. of a family of logics for verifying HMMs, called POCTL*, and its model checking algorithm. As far as we know, the verification tool we present here is the first one based on Zhang et al.'s approach. As an example of its effective application, we verify properties of a handover task in the context of human-robot interaction. Our tool was implemented in Haskell, and the experimental evaluation was performed using the humanoid robot Bert2.