Robots as powerful allies for the study of embodied cognition from the bottom up

Hoffmann, Matej; Pfeifer, Rolf

doi:10.1093/oxfordhb/9780198735410.013.45

Cited by 23 publications

(18 citation statements)

References 68 publications

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“…With respect to perception this means to develop deep learning approaches that use parts and their relationships to better explain overall scenes [10]. With respect to robotics, there is the approach of embodied AI that aims at exploiting the robot body to better understand a situation [11]. We also refer to this approach as situated, since the robot is immersed in its settings and exploits contextual knowledge to improve its understanding of the scenes.…”

Section: Situated Robot Perception: Embodied Ai From the View Ofmentioning

confidence: 99%

Learn, detect, and grasp objects in real-world settings

Vincze

Patten

Park

et al. 2020

Elektrotech. Inftech.

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Experts predict that future robot applications will require safe and predictable operation: robots will need to be able to explain what they are doing to be trusted. To reach this goal, they will need to perceive their environment and its object to better understand the world and the tasks they have to perform. This article gives an overview of present advances with the focus on options to learn, detect, and grasp objects. With the approach of colour and depth (RGB-D) cameras and the advances in AI and deep learning methods, robot vision has been pushed considerably over the last years. We summarise recent results for pose estimation of objects and work on verifying object poses using a digital twin and physics simulation. The idea is that any hypothesis from an object detector and pose estimator is verified leveraging on the continuous advances in deep learning approaches to create object hypotheses. We then show that the object poses are robust enough such that a mobile manipulator can approach the object and grasp it. We intend to indicate that it is now feasible to model, recognise and grasp many objects with good performance, though further work is needed for applications in industrial settings.

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Section: Situated Robot Perception: Embodied Ai From the View Ofmentioning

confidence: 99%

Learn, detect, and grasp objects in real-world settings

Vincze

Patten

Park

et al. 2020

Elektrotech. Inftech.

View full text Add to dashboard Cite

show abstract

“…On the other hand, the relation between sensorimotor contingencies and the development of early body knowledge per se is still poorly studied in robotics. Hoffmann and colleagues in different studies has underlined how the development of the body schema may improve the functioning of artificial agents (Hoffmann et al, 2010;Roncone et al 2014), as well as how robots can be used to model early sensorimotor development (Hoffmann et al, 2017;Hoffmann and Pfeifer 2018). The way peripersonal motor maps can help robot learning has been studied in Jamone et al (2014).…”

Section: Approaches In Roboticsmentioning

confidence: 99%

Sensorimotor contingencies as a key drive of development: from babies to robots

Jacquey¹,

Baldassarre²,

Santucci³

et al. 2019

Preprint

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Much current work in robotics focuses on the development of robots capable of autonomous unsupervised learning. An essential prerequisite for such learning to be possible is that the agent should be sensitive to the link between its actions and the consequences of its actions, called sensorimotor contingencies. This sensitivity, and more particularly its role as a key drive of development, has been widely studied by developmental psychologists. However, the results of these studies may not necessarily be accessible or intelligible to roboticians. In this paper, we review the main experimental data demonstrating the role of sensitivity to sensorimotor contingencies in infants’ acquisition of four fundamental motor and cognitive abilities: body knowledge, memory, generalization and goal-directedness. We relate this data from development psychology to work in robotics, highlighting the links between these two domains of research. In the last part of the article we present a blueprint architecture demonstrating how exploitation of sensitivity to sensorimotor contingencies, combined with the notion of "goal", allows an agent to develop new sensorimotor skills. This architecture can be used to guide the design of specific computational models, and also to possibly envisage new empirical experiments.

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“…In particular, the distance between two information channels allowed to infer the topology of sensors and motors in an AIBO robot. Looking at the formation of body maps, Hoffmann and colleagues in different studies have underlined how the development of the body schema may improve the functioning of artificial agents (Hoffmann et al, 2010; Roncone et al, 2014), as well as how robots can be used to model early sensorimotor development (Hoffmann et al, 2017; Hoffmann and Pfeifer, 2018). The way peripersonal motor maps can help robot learning has been studied in Jamone et al (2014).…”

Section: Body Knowledge: Is the Agent Able To Identify The Particularmentioning

confidence: 99%

Sensorimotor Contingencies as a Key Drive of Development: From Babies to Robots

et al. 2019

View full text Add to dashboard Cite

Much current work in robotics focuses on the development of robots capable of autonomous unsupervised learning. An essential prerequisite for such learning to be possible is that the agent should be sensitive to the link between its actions and the consequences of its actions, called sensorimotor contingencies. This sensitivity, and more particularly its role as a key drive of development, has been widely studied by developmental psychologists. However, the results of these studies may not necessarily be accessible or intelligible to roboticians. In this paper, we review the main experimental data demonstrating the role of sensitivity to sensorimotor contingencies in infants’ acquisition of four fundamental motor and cognitive abilities: body knowledge, memory, generalization, and goal-directedness. We relate this data from developmental psychology to work in robotics, highlighting the links between these two domains of research. In the last part of the article we present a blueprint architecture demonstrating how exploitation of sensitivity to sensorimotor contingencies, combined with the notion of “goal,” allows an agent to develop new sensorimotor skills. This architecture can be used to guide the design of specific computational models, and also to possibly envisage new empirical experiments.

show abstract

Robots as powerful allies for the study of embodied cognition from the bottom up

Cited by 23 publications

References 68 publications

Learn, detect, and grasp objects in real-world settings

Learn, detect, and grasp objects in real-world settings

Sensorimotor contingencies as a key drive of development: from babies to robots

Sensorimotor Contingencies as a Key Drive of Development: From Babies to Robots

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