High-frequency, low-latency, 6-DoF object tracking is useful for grasping objects in motion, taking robots beyond pick-and-place tasks. We propose using an event-camera for tracking the objects to leverage the low-latency and continuous (i.e. not fixed-rate) data capture for high-frequency tracking. We propose the EDOPT algorithm, which maintains real-time operation with a variable event-rate (which occurs due to variation in camera velocity and scene texture) and avoids frame-jumps and motion-blur which are problematic in traditional computer vision solutions. EDOPT uses a strong object prior, leading to a novel solution possible only with the event-camera. To our knowledge, this is the first method for 6-DoF object pose estimation with only the event-camera. The proposed method achieves comparable results to a state-of-the-art DNN technique that fuses frames, depth, and events. We demonstrate smooth, online object pose tracking with a live camera feed at > 300 Hz.

Achieving natural and engaging verbal interactions is one of the main challenges faced by Social Robotics. In this context, physical embodiment may be one of the most critical factors: indeed, previous work indicates that physical robots elicit more favorable social responses than virtual agents. However, the effects of physical embodiment have been analysed only in some specific and limited scenarios, where verbal interaction was reduced to basic commands.The current work aims at investigating the effect of robots' physical embodiment in a pure conversation task, by considering some relevant aspects of social interaction, such as usability, speech interface quality, user satisfaction and engagement. To this aim, a pilot experiment where participants were required to chitchat with a robot and a smartphone app, both connected to the same conversation framework, has been carried out. Preliminary results are presented and discussed, and they offer interesting insights about the positive effects of physical embodiment on some of the analysed aspects.