Object–Action Complexes: Grounded abstractions of sensory–motor processes

Krüger, Norbert; Geib, Christopher W.; Piater, Justus; Petrick, Ronald P. A.; Steedman, Mark; Wörgötter, Florentin; Ude, Aleš; Asfour, Tamim; Kraft, Dirk; Omrcen, Damir; Agostini, Alejandro; Dillmann, Rüdiger

doi:10.1016/j.robot.2011.05.009

Cited by 148 publications

(89 citation statements)

References 47 publications

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“…To verify if the entropy-based action selection policy (MinEnt) improves classification, we compare this policy where the action is given by (8) to randomly selected actions (Random). Since the dataset is limited in number of samples, we proceed by bootstrapping, instead of having two separate training and test sets.…”

Section: Classification With Optimal Action Selectionmentioning

confidence: 99%

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Interactive object classification using sensorimotor contingencies

Högman

Björkman

Kragić

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Understanding and representing objects and their function is a challenging task. Objects we manipulate in our daily activities can be described and categorized in various ways according to their properties or affordances, depending also on our perception of those. In this work, we are interested in representing the knowledge acquired through interaction with objects, describing these in terms of action-effect relations, i.e. sensorimotor contingencies, rather than static shape or appearance representations. We demonstrate how a robot learns sensorimotor contingencies through pushing using a probabilistic model. We show how functional categories can be discovered and how entropy-based action selection can improve object classification.

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Section: Classification With Optimal Action Selectionmentioning

confidence: 99%

“…A parallel can be drawn to Object-Action Complexes (OACs) [8]. OACs have been proposed as bricks for cognitive architectures and are formalized to include an object and …”

Section: Introductionmentioning

confidence: 99%

Interactive object classification using sensorimotor contingencies

Högman

Björkman

Kragić

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…The action was associated with the symbolic name given to the action. Transferring the knowledge to a high-level cognitive system could also be achieved with a symbolic representation that captured the interaction between objects and actions [31,32]. However, in the present study, we assumed that the teacher supplied the name of the action that he executed for the robot.…”

Section: Robot Platformmentioning

confidence: 99%

Relational reinforcement learning with guided demonstrations

Martínez

Alenyà

Torras

2017

Artificial Intelligence

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Model-based reinforcement learning is a powerful paradigm for learning tasks in robotics. However, in-depth exploration is usually required and the actions have to be known in advance. Thus, we propose a novel algorithm that integrates the option of requesting teacher demonstrations to learn new domains with fewer action executions and no previous knowledge. Demonstrations allow new actions to be learned and they greatly reduce the amount of exploration required, but they are only requested when they are expected to yield a significant improvement because the teacher's time is considered to be more valuable than the robot's time. Moreover, selecting the appropriate action to demonstrate is not an easy task, and thus some guidance is provided to the teacher. The rule-based model is analyzed to determine the parts of the state that may be incomplete, and to provide the teacher with a set of possible problems for which a demonstration is needed. Rule analysis is also used to find better alternative models and to complete subgoals before requesting help, thereby minimizing the number of requested demonstrations. These improvements were demonstrated in a set of experiments, which included domains from the international planning competition and a robotic task. Adding teacher demonstrations and rule analysis reduced the amount of exploration required by up to 60% in some domains, and improved the success ratio by 35% in other domains.

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“…For a successful integration of these techniques in a robotic platform, it is mandatory to ground the symbolic descriptions of the AI planning methods to let the robot interact with the real world in order to execute a task [12]. This is normally done by integrating methods of different levels of abstractions [10,13,14,15,16], ranging from purely symbolic methods [17], to sensor information processing [18] and acting methods [19]. Several approaches integrate planning and acting for the robotic execution of human like tasks.…”

Section: Related Workmentioning

confidence: 99%

A cognitive architecture for automatic gardening

Agostini

Aleny

Fischbach

et al. 2017

Computers and Electronics in Agriculture

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Keywords: cognitive architecture, optimized plant treatments, automatic gardening, human-robot interaction, learning planning operators. AbstractIn large industrial greenhouses, plants are usually treated following well established protocols for watering, nutrients, and shading/light. While this is practical for the automation of the process, it does not tap the full potential for optimal plant treatment. To more efficiently grow plants, specific treatments according to the plant individual needs should be applied. Experienced human gardeners are very good at treating plants individually. Unfortunately, hiring a crew of gardeners to carry out this task in large greenhouses is not cost effective. In this work we present a cognitive system that integrates artificial intelligence (AI) techniques for decision-making with robotics techniques for sensing and acting to autonomously treat plants using a real-robot platform. Artificial intelligence techniques are used to decide the amount of water and nutrients each plant needs according to the history of the plant. Robotic techniques for sensing measure plant attributes (e.g. leaves) from visual information using 3D model representations. These attributes are used by the AI system to make decisions about the treatment to apply. Acting techniques execute robot movements to supply the plants with the specified amount of water and nutrients.

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Object–Action Complexes: Grounded abstractions of sensory–motor processes

Cited by 148 publications

References 47 publications

Interactive object classification using sensorimotor contingencies

Interactive object classification using sensorimotor contingencies

Relational reinforcement learning with guided demonstrations

A cognitive architecture for automatic gardening

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