Strategies for modelling open-loop saccade control of a cable-driven biomimetic robot eye

In human-robot interactions, eye movements play an important role in non-verbal communication. However, controlling the motions of a robotic eye that display similar performance as the human oculomotor system is still a major challenge. In this paper, we study how to control a realistic model of the human eye, with a cable-driven actuation system that mimicks the 6 extra-ocular muscles. We have built a robotic prototype and developed a non-linear simulation model, for which we compared different techniques to control its gaze behavior to match the main characteristics of saccade eye movements. In the first approach, we linearized the six degrees of freedom nonlinear model, using a local derivative technique, and designed linear-quadratic optimal controllers to optimize a cost function that accounts for accuracy, energy and duration. The second method learns a dynamic neural-network that matches the system dynamics, trained from sample trajectories of the system, and a non-linear trajectory optimization solver optimized a similar cost function. We focused on the generation of rapid saccadic eye movements with fully unconstrained kinematics, and the generation of control signals for the six cables that simultaneously satisfied several dynamic optimization criteria. The model faithfully mimicked the three-dimensional rotational kinematics and dynamics observed for human saccades. Our experimental results indicate that while the linear model provides a more accurate eye movement, the nonlinear model simulate eye dynamic properties in a better way faithful approximation to the properties of the human saccadic system than the linearized model, at the cost of larger training and optimization time.

ROAD: The ROad event Awareness Dataset for Autonomous Driving

Humans approach driving in a holistic fashion which entails, in particular, understanding road events and their evolution. Injecting these capabilities in an autonomous vehicle has thus the potential to take situational awareness and decision making closer to human-level performance. To this purpose, we introduce the ROad event Awareness Dataset (ROAD) for Autonomous Driving, to our knowledge the first of its kind. ROAD is designed to test an autonomous vehicle's ability to detect road events, defined as triplets composed by a moving agent, the action(s) it performs and the corresponding scene locations. ROAD comprises 22 videos, originally from the Oxford RobotCar Dataset, annotated with bounding boxes showing the location in the image plane of each road event. We also provide as baseline a new incremental algorithm for online road event awareness, based on inflating RetinaNet along time, which achieves a mean average precision of 16.8% and 6.1% for frame-level and video-level event detection, respectively, at 50% overlap. Though promising, these figures highlight the challenges faced by situation awareness in autonomous driving. Finally, ROAD allows scholars to investigate exciting tasks such as complex (road) activity detection, future road event anticipation and the modelling of sentient road agents in terms of mental states. Dataset can be obtained from https://github.com/gurkirt/road-dataset and baseline code from https://github.com/gurkirt/3D-RetinaNet.