Haptic bilateral teleoperation technology aims to transmit the sensation of touch over the internet, enabling remote work for all. However, it faces significant challenges, including stringent latency requirements (sub 1 ms) for the Tactile Internet and the need for advanced models in Model Mediated Teleoperation. This work explored the feasibility of haptic bilateral teleoperation over long distances through a new framework that leverages live video and predictive haptic feedback. This framework was implemented on a real experimental setup where users draw with a haptic device controlling a remote robotic arm on a moving whiteboard. A user study was conducted on this setup which confirmed that the method is sound and revealed that force feedback latency requirements are significantly more stringent than those of visual feedback. In this application users did not perceive the application as more difficult at 50 ms network latency (110 ms visual latency) with user experience only degrading at 75 ms added network latency. These findings prove that through the Demonstrate Imitate Adjust framework, long-distance haptic bilateral teleoperation is achievable.

Robotic swarms provide a great many uses within a world increasingly relying on autonomous systems. Alas these swarms are also very vulnerable to faults, even the smallest fault can cripple the performance of a whole swarm. Such a fault could be one of several types; those that have to do with sensors, communication, ... This paper will try to discover the effect that these faults have on the performance of a robotic swarm attempting to complete a specified task, e.g. a multiple destination routing problem or a variant of a foraging task. With the experiments concluded and the data gathered it proved possible to observe correlations between fault and performance, but this dependency heavily changes based on the algorithm used and fault introduced.