Emanuel Sousa

Off-line simulation inspires insight

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.

The power of prediction: Robots that read intentions

Humans are experts in cooperating in a smooth and proactive manner. Action and intention understanding are critical components of efficient joint action. In the context of the EU Integrated Project JAST [16] we have developed an anthropomorphic robot endowed with these cognitive capacities. This project and respective robot (ARoS) is the focus of the video. More specifically, the results illustrate crucial cognitive capacities for efficient and successful human-robot collaboration such as goal inference, error detection and anticipatory action selection. Results were considered one of the ICT “success stories”[22].

Learning a musical sequence by observation: A robotics implementation of a dynamic neural field model

We tested in a robotics experiment a dynamic neural field model for learning a precisely timed musical sequence. Based on neuro-plausible processing mechanisms, the model implements the idea that order and relative timing of events are stored in an integrated representation whereas the onset of sequence production is controlled by a separate process. Dynamic neural fields provide a rigorous theoretical framework to analyze and implement the necessary neural computations that bridge gaps between sensation and action in order to mediate working memory, action planing, and decision making. The robot first memorizes a short musical sequence performed by a human teacher by watching color coded keys on a screen, and then tries to execute the piece of music on a keyboard from memory without any external cues. The experimental results show that the robot is able to correct in very few demonstration-execution cycles initial sequencing and timing errors.

MUVTIME: a Multivariate time series visualizer for behavioral science

Marie Curie International Incoming Fellowship PIIF-GA-2011- 301155; Portuguese Foundation for Science and Technology (FCT) project PTDC/PSI- PCO/121494/2010; AFP was also partially funded by the FCT project (IF/00217/2013)

On observational learning of hierarchies in sequential tasks: a dynamic neural field model

FP7 project NETT // Portuguese FCT Grant SFRH/BD/48529/2008, financed by POPH-QREN-Type 4.1-Advanced Training, co-funded by the European Social Fund and national funds from MEC; (2) FEDER Funds through Competitivity Factors Operational Program - COMPETE and National Funds by FCT Portuguese Science and Technology Foundation under the Project FCOMP-01-0124-FEDER-022674. (2) Project NETT: Neural Engineering Transformative Technologies, EU-FP7 ITN proj. nr. 289146

Visual-vestibular and postural analysis of motion sickness, panic, and acrophobia

National funds and co-financed by FEDER through COMPETE2020 under the PT2020 Partnership Agreement (POCI-01-0145-FEDER-007653). FCT/3599-PPCDT/121494/PT

Special session: Dynamic interactions between visual experiences, actions and word learning

The primary aim of this special session is to inform the conferences interdisciplinary audience about the state-of-the-art in developmental studies of action and language interactions. Action and language develop in parallel, impacting each other, and as such, bootstrap action, social, and cognitive development. We will present recent empirical evidence on developmental dependencies between visual experiences and word learning, followed by discussion of potential implications of this research for embodied theories of action and language integration.