Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning

There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.

Operation modes for the electric vehicle in smart grids and smart homes : present and proposed modes

This paper presents the main operation modes for an electric vehicle (EV) battery charger framed in smart grids and smart homes, i.e., are discussed the present-day and are proposed new operation modes that can represent an asset towards EV adoption. Besides the well-known grid to vehicle (G2V) and vehicle to grid (V2G), this paper proposes two new operation modes: Home-to-vehicle (H2V), where the EV battery charger current is controlled according to the current consumption of the electrical appliances of the home (this operation mode is combined with the G2V and V2G); Vehicle-for-grid (V4G), where the EV battery charger is used for compensating current harmonics or reactive power, simultaneously with the G2V and V2G operation modes. The vehicle-to-home (V2H) operation mode, where the EV can operate as a power source in isolated systems or as an off-line uninterruptible power supply to feed priority appliances of the home during power outages of the electrical grid is presented in this paper framed with the other operation modes. These five operation modes were validated through experimental results using a developed 3.6 kW bidirectional EV battery charger prototype, which was specially designed for these operation modes. The paper describes the developed EV battery charger prototype, detailing the power theory and the voltage and current control strategies used in the control system. The paper presents experimental results for the various operation modes, both in steady-state and during transients.