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Finding Your Way from the Bed to the Kitchen: Reenacting and Recombining Sensorimotor Episodes Learned from Human Demonstration

Artikel i vetenskaplig tidskrift
Författare E. A. Billing
H. Svensson
Robert Lowe
T. Ziemke
Publicerad i Frontiers in Robotics and Ai
Volym 3
ISSN 2296-9144
Publiceringsår 2016
Publicerad vid Institutionen för tillämpad informationsteknologi (GU)
Språk en
Länkar dx.doi.org/10.3389/frobt.2016.00009
Ämnesord embodied cognition, imagination, internal simulation, learning from demonstration, representation, simulation theory, predictive sequence learning, prospection, motor representations, simulation, perception, model, navigation, cognition, behavior, systems, imagery, architecture, Robotics
Ämneskategorier Data- och informationsvetenskap

Sammanfattning

Several simulation theories have been proposed as an explanation for how humans and other agents internalize an "inner world" that allows them to simulate interactions with the external real world - prospectively and retrospectively. Such internal simulation of interaction with the environment has been argued to be a key mechanism behind mentalizing and planning. In the present work, we study internal simulations in a robot acting in a simulated human environment. A model of sensory-motor interactions with the environment is generated from human demonstrations and tested on a Robosoft Kompai robot. The model is used as a controller for the robot, reproducing the demonstrated behavior. Information from several different demonstrations is mixed, allowing the robot to produce novel paths through the environment, toward a goal specified by top-down contextual information. The robot model is also used in a covert mode, where the execution of actions is inhibited and perceptions are generated by a forward model. As a result, the robot generates an internal simulation of the sensory-motor interactions with the environment. Similar to the overt mode, the model is able to reproduce the demonstrated behavior as internal simulations. When experiences from several demonstrations are combined with a top-down goal signal, the system produces internal simulations of novel paths through the environment. These results can be understood as the robot imagining an "inner world" generated from previous experience, allowing it to try out different possible futures without executing actions overtly. We found that the success rate in terms of reaching the specified goal was higher during internal simulation, compared to overt action. These results are linked to a reduction in prediction errors generated during covert action. Despite the fact that the model is quite successful in terms of generating covert behavior toward specified goals, internal simulations display different temporal distributions compared to their overt counterparts. Links to human cognition and specifically mental imagery are discussed.

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