The learning and use of traversability affordance using range images on a mobile robot

Uğur, Emre; Dogar, Mehmet R.; Çakmak, Maya; Şahi̇n, Erol

doi:10.1109/robot.2007.363571

Cited by 49 publications

(44 citation statements)

References 12 publications

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“…[16], [9]), the signatures approach differs by the use of interactions, which allows implicit relations between interactions, and thus, spatial properties of the environment, to be discovered.…”

Section: A Signatures Of Interactionsmentioning

confidence: 99%

“…Point 1) is well studied in literature. For example, Pfeifer and Scheier [12], Ugur et al [16] and Baleia [1] proposed approaches where an agent learns to define and classify objects of its environment through possibilities of interaction (such as "walkthroughable" or not). However, these approaches do not let the possibility to generate spatial behaviors.…”

Section: Introductionmentioning

confidence: 99%

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Autonomous object modeling and exploiting: A new approach based on affordances from continual interaction with environment

Mille

Cordier

2017

2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)

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To cite this version:Simon Gay, Alain Mille, Amélie Cordier. Autonomous object modeling and exploiting: a new approach based on affordances from continual interaction with environment. Abstract-We present an architecture for self-motivated agents to generate behaviors in an environment that is continuous, both in space and time, through a continual interaction process. The long-term goal is to design agents that construct their own knowledge of objects and space through experience of the environment, rather than exploiting pre-coded knowledge. The agent exploits this constructed knowledge to exhibit behaviors satisfying its self-motivated principles, based on valences attributed to interactions, that specify inborn behavioral preferences. Over time, the agent learns the relation between its perception of objects and the interactions that they afford, in the form of data structures, called signatures of interaction. The agent keeps track of enacted interactions in a spatial memory in which it can use signatures to recognize and localize distant possibilities of interactions, and exhibits behaviors that satisfy its motivation principles, accordingly to this approach. In this paper, we propose a continual decision cycle between an agent and its environment to cope with the constraints that an artificial agent would meet in a physical environment.

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Section: A Signatures Of Interactionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Autonomous object modeling and exploiting: A new approach based on affordances from continual interaction with environment

Mille

Cordier

2017

2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)

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“…In another study, Montesano et al [17] created a framework with which a robot learned the similarity between differently sized spheres and cubes by learning relationships between the robot's interactions, the object's features, and the observed effects. In the work of Ugur et al [18], a simulated robot traversed an environment with random dispersions of spheres, cubes, and cylinders. It learned which objects afforded traversability (spheres and cylinders in lying orientations) from those that did not (cubes and cylinders in upright orientations).…”

Section: Related Workmentioning

confidence: 99%

“…It learned which objects afforded traversability (spheres and cylinders in lying orientations) from those that did not (cubes and cylinders in upright orientations). None of the robots in [16], [17], or [18] performed explicit object categorization.…”

Section: Related Workmentioning

confidence: 99%

How to separate containers from non-containers? a behavior-grounded approach to acoustic object categorization

Griffith

Sinapov

Sukhoy

et al. 2010

2010 IEEE International Conference on Robotics and Automation

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Abstract-This paper describes an approach to interactive object categorization that couples exploratory behaviors and their resulting acoustic signatures to form object categories. The framework was tested with an upper-torso humanoid robot on a container/non-container categorization task. The robot used six exploratory behaviors (drop block, grasp, move, shake, flip, and drop object) and applied them to twenty objects. The results from this large-scale experimental study show that the robot was able to learn meaningful object categories using only acoustic information. The results also show that the quality of the categorization depends on the exploratory behavior used to derive it as some behaviors elicit more salient acoustic signatures than others.

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“…Uǧur, et al evaluated traversability for determining navigation motions of robots [6]. Although these works have shown highly effective results, they contain two issues.…”

Section: Introductionmentioning

confidence: 99%

Motion generation based on reliable predictability using self-organized object features

Nishide

Ogata

Tani

et al. 2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Predictability is an important factor for determining robot motions. This paper presents a model to generate robot motions based on reliable predictability evaluated through a dynamics learning model which self-organizes object features. The model is composed of a dynamics learning module, namely Recurrent Neural Network with Parametric Bias (RNNPB), and a hierarchical neural network as a feature extraction module. The model inputs raw object images and robot motions. Through bi-directional training of the two models, object features which describe the object motion are self-organized in the output of the hierarchical neural network, which is linked to the input of RNNPB. After training, the model searches for the robot motion with high reliable predictability of object motion. Experiments were performed with the robot's pushing motion with a variety of objects to generate sliding, falling over, bouncing, and rolling motions. For objects with single motion possibility, the robot tended to generate motions that induce the object motion. For objects with two motion possibilities, the robot evenly generated motions that induce the two object motions.

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The learning and use of traversability affordance using range images on a mobile robot

Cited by 49 publications

References 12 publications

Autonomous object modeling and exploiting: A new approach based on affordances from continual interaction with environment

Autonomous object modeling and exploiting: A new approach based on affordances from continual interaction with environment

How to separate containers from non-containers? a behavior-grounded approach to acoustic object categorization

Motion generation based on reliable predictability using self-organized object features

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