Prediction of Undesired Situations Based on Multi-Modal Representations

Lara, Bruno; Rendón, Juan M.

doi:10.1109/cerma.2006.75

Cited by 4 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A trained robot equipped with the proposed framework was able, in some cases, to move blindly in a simple environment, using as input only own sensory predictions rather than actual sensory input. Lara and Rendon-Mancha (2006) equipped a simulated agent with a forward model implemented as an artificial neural network. The system learned to successfully predict multimodal sensory representations formed by visual and tactile stimuli for an obstacle avoidance task.…”

Section: Computational Models For Sensorimotor Simulationsmentioning

confidence: 99%

Exploration Behaviors, Body Representations, and Simulation Processes for the Development of Cognition in Artificial Agents

2016

Self Cite

View full text Add to dashboard Cite

Sensorimotor control and learning are fundamental prerequisites for cognitive development in humans and animals. Evidence from behavioral sciences and neuroscience suggests that motor and brain development are strongly intertwined with the experiential process of exploration, where internal body representations are formed and maintained over time. In order to guide our movements, our brain must hold an internal model of our body and constantly monitor its configuration state. How can sensorimotor control enable the development of more complex cognitive and motor capabilities? Although a clear answer has still not been found for this question, several studies suggest that processes of mental simulation of action-perception loops are likely to be executed in our brain and are dependent on internal body representations. Therefore, the capability to re-enact sensorimotor experience might represent a key mechanism behind the implementation of higher cognitive capabilities, such as behavior recognition, arbitration and imitation, sense of agency, and self-other distinction. This work is mainly addressed to researchers in autonomous motor and mental development for artificial agents. In particular, it aims at gathering the latest developments in the studies on exploration behaviors, internal body representations, and processes of sensorimotor simulations. Relevant studies in human and animal sciences are discussed and a parallel to similar investigations in robotics is presented.

show abstract

Section: Computational Models For Sensorimotor Simulationsmentioning

confidence: 99%

Exploration Behaviors, Body Representations, and Simulation Processes for the Development of Cognition in Artificial Agents

2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…In Cognitive Robotics, internal models have been used for action execution and recognition (Dearden, 2008), safe navigation planning (Lara and Rendon-Mancha, 2006;Möller and Schenck, 2008), and saccades control (Schenck et al, 2011). On the other hand, several works have proposed IM-FM couplings to perform different tasks.…”

Section: Internal Modelsmentioning

confidence: 99%

“…First, we find that current implementations of internal models lack of flexibility as a consequence of the computational tools used. This translates into the fact that learning plasticity is highly reduced or even absent [e.g., see Lara and Rendon-Mancha (2006), Dearden (2008), Möller and Schenck (2008), and Schenck et al (2011)]. Second, the implementations redound in ad hoc inverse and forward models, not easily scalable, and in some cases, using different networks for different motor commands [e.g., Möller and Schenck (2008)].…”

Section: Introductionmentioning

confidence: 99%

A Self-Organized Internal Models Architecture for Coding Sensory–Motor Schemes

et al. 2016

View full text Add to dashboard Cite

Cognitive robotics research draws inspiration from theories and models on cognition, as conceived by neuroscience or cognitive psychology, to investigate biologically plausible computational models in artificial agents. In this field, the theoretical framework of Grounded Cognition provides epistemological and methodological grounds for the computational modeling of cognition. It has been stressed in the literature that simulation, prediction, and multi-modal integration are key aspects of cognition and that computational architectures capable of putting them into play in a biologically plausible way are a necessity. Research in this direction has brought extensive empirical evidence, suggesting that Internal Models are suitable mechanisms for sensory-motor integration. However, current Internal Models architectures show several drawbacks, mainly due to the lack of a unified substrate allowing for a true sensory-motor integration space, enabling flexible and scalable ways to model cognition under the embodiment hypothesis constraints. We propose the Self-Organized Internal Models Architecture (SOIMA), a computational cognitive architecture coded by means of a network of self-organized maps, implementing coupled internal models that allow modeling multi-modal sensorymotor schemes. Our approach addresses integrally the issues of current implementations of Internal Models. We discuss the design and features of the architecture, and provide empirical results on a humanoid robot that demonstrate the benefits and potentialities of the SOIMA concept for studying cognition in artificial agents.

show abstract