Performing actions in a timely manner is an indispensable aspect in everyday human activities. Accordingly, it has to be present in robotic systems if they are going to seamlessly interact with humans. The current work addresses the problem of learning both the spatial and temporal characteristics of human motions from observation. We formulate learning as a mapping between two worlds (the observed and the action ones). This mapping is realized via an abstract intermediate representation termed “Latent Space.” Learned actions can be subsequently invoked in the context of more complex human–robot interaction (HRI) scenarios. Unlike previous learning from demonstration (LfD) methods that cope only with the spatial features of an action, the formulated scheme effectively encompasses spatial and temporal aspects. Learned actions are reproduced under the high-level control of a time-informed task planner. During the implementation of the studied scenarios, temporal and physical constraints may impose speed adaptations in the reproduced actions. The employed latent space representation readily supports such variations, giving rise to novel actions in the temporal domain. Experimental results demonstrate the effectiveness of the proposed scheme in the implementation of HRI scenarios. Finally, a set of well-defined evaluation metrics are introduced to assess the validity of the proposed approach considering the temporal and spatial consistency of the reproduced behaviors.

This paper proposes a new kinematic controller for the waypoints guidance of robotic mobile platforms. A notable feature of the controller is its ability to process the raw sequence of waypoints to produce smooth reference velocities from control laws that are derived by taking into account a driving profile including the velocity limits, the acceleration limits, the motion modes through each waypoint (forward or backward) and the precision constraints that are required to ensure accurate waypoints traversal. A mathematical analysis demonstrates the convergence of the movements through the waypoints sequence. In addition, we present a simple way to adapt the driving profile in order that the platform reaches the last waypoint at a prescribed time. A feed-forward unit is finally described, that compensates for delays and first-order poles in the velocity response of the platform. Various simulations and experiments on real robotic platforms demonstrate the behavior and the effectiveness of the solution.

Computing a realistic velocity profile for a mobile robot is a challenging task due to the large number of kinematic and dynamic constraints involved. In order for a mobile robot to complete its task it must be able to plan and follow a trajectory. It may also be necessary to follow a given velocity profile, depending on the environment. Temporal planning, or following a given velocity profile, can be used to minimize time of motion and to avoid moving obstacles. For example, assuming the mobile robot is a smart wheelchair, it must follow a prescribed path while following a strict speed limit. This paper presents a temporal planning algorithm that is implemented on a wheeled mobile robot to be used in an indoor setting, such as a hospital ward. The path planning stage is accomplished by using cubic spline functions. A trajectory is created by assigning an arbitrary time of 1 s to each segment of the path. This trajectory is made feasible by applying a number of constraints and using a linear scaling technique. When a velocity profile is given, a non-linear time scaling technique is used to fit the mobile robot's linear velocity to the given velocity profile. A method for avoiding moving obstacles is also implemented. Simulation and experimental results showed good agreement with each other. The main contribution of this paper is in developing a temporal planning algorithm, which is capable of moving on an uneven surface (graded non-flat), and its implementation on the mobile robot at the robotics lab in the University of Saskatchewan. This algorithm is computationally very efficient as it requires low computation cost and does not involve major iterations.