Long distance Haptic Bilateral Teleoperation (HBT) is used in applications such as surgery, training, remote operation, and disaster relief. One of the main challenges in these systems is communication delay. When force feedback relies directly on sensors in the remote environment, increasing delay quickly makes the system unstable and hard to control. To overcome this, previous approaches generated force feedback using a local simulation of the remote environment, while providing visual feedback through live video [20]. This reduces timing constraints, but only works when the simulation accurately represents reality. When the robot encounters objects that are not modeled and not visible to the operator, unpredictable forces occur and the system no longer behaves correctly.
This thesis investigates how such unpredictable forces can be handled in delayed HBT. We introduce a method that keeps predictive force feedback through simulation, while correcting the simulation using sensor data from the robot. A small 3D printed attachment was developed to detect unexpected contact events. These measurements are sent back to the operator side and used to update the virtual environment, allowing force feedback to be generated even for unknown objects. The approach was evaluated in a user study with 13 participants under varying delays and visibility conditions. The results show that when visibility is limited, reaction based feedback can be used instead of prediction based feedback. The findings indicate that combining simulation based prediction with remote sensing offers a practical solution for dealing with unpredictable forces in long distance HBT.

Federated learning provides a lot of opportunities, especially with the built-in privacy considerations. There is however one attack that might compromise the utility of federated learning: backdoor attacks [14]. There are already some existing defenses, like flame [13] but they are computationally expensive [14]. This paper evaluates a version of differential privacy, where the Gaussian noise added to the aggravated model of the clipped updates is smaller than usually. This is often referred to as weak differential privacy or weakDP. This paper evaluates weakDP with different parameters to find if weakDP can be used as a defense for a language processing federated learning classifier against a backdoor attack.