Robust modeling of dynamic environment based on robot embodiment

Noda, Kuniaki; Suzuki, Mototaka; Tsuchiya, N.; Suga, Yuki; Ogata, Tetsuya; Sugano, Shigeki

doi:10.1109/robot.2003.1242142

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Merriam-Webster defines coherence as a "systematic or logical connection or consistency", but in each domain the definition varies to some degree. In robotics the term has been used in [5], [6], [7] but even in these usages, a process to quantify coherence is not evident.…”

Section: Introductionmentioning

confidence: 99%

Quantifying coherence when learning behaviors via teleoperation

Remy

Howard

2008

RO-MAN 2008 - The 17th IEEE International Symposium on Robot and Human Interactive Communication

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Applications of robotics are quickly changing. Just as computer use evolved from research purposes to everyday functions, applications of robotics are making a transition to mainstream usage. With this change in applications comes a change in the user base of robotics, and there is a pronounced move to reduce the complexity of robotic control. The move to reduce complexity is linked to the separation of the role of robot designer and robot operator.For many target applications, the operator of the robot needs to be able to correct and augment its capabilities. One method to enable this is learning from human data, which has already been successfully applied to robotics. We assert that this learning process is only viable when the demonstrated human behavior is coherent. In this work we test the hypothesis that quantifying the coherence in the provided instruction can provide useful information about the progress of the learning process.We discuss results from the application of this method to reactive behaviors. Such behaviors permit the learning process to be computationally tractable in real-time. These results support the hypothesis that coherence is important for this type of learning and also show that this property can be used to provide an avenue for self regulation of the learning process.

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Section: Introductionmentioning

confidence: 99%

Quantifying coherence when learning behaviors via teleoperation

Remy

Howard

2008

RO-MAN 2008 - The 17th IEEE International Symposium on Robot and Human Interactive Communication

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“…Noda et al integrated multiple static features (size, weight, and color images) for object recognition while grasping the object with its hands [2]. The work used a three-layered SOM (Self-Organizing Map [3]) taking into account only the static features, which entails quite an arduous task for applying the results to motion planning where dynamics of objects need to be regarded.…”

Section: Introductionmentioning

confidence: 99%

Predicting Object Dynamics from Visual Images through Active Sensing Experiences

Nishide

Ogata

Tani

et al. 2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

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Prediction of dynamic features is an important task for determining the manipulation strategies of an object. This paper presents a technique for predicting dynamics of objects relative to the robot's motion from visual images. During the learning phase, the authors use Recurrent Neural Network with Parametric Bias (RNNPB) to self-organize the dynamics of objects manipulated by the robot into the PB space. The acquired PB values, static images of objects, and robot motor values are input into a hierarchical neural network to link the static images to dynamic features (PB values). The neural network extracts prominent features that induce each object dynamics. For prediction of the motion sequence of an unknown object, the static image of the object and robot motor value are input into the neural network to calculate the PB values. By inputting the PB values into the closed loop RNNPB, the predicted movements of the object relative to the robot motion are calculated sequentially. Experiments were conducted with the humanoid robot Robovie-IIs pushing objects at different heights. Reducted grayscale images and shoulder pitch angles were input into the neural network to predict the dynamics of target objects. The results of the experiment proved that the technique is efficient for predicting the dynamics of the objects.

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