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Abstract: We describe an unsupervised on-line method for learning of manipulative actions that allows a robot to push an object connected to it with a rotational point contact to a desired point in image-space. By observing the results of its actions on the object's orientation in imagespace, the system forms a predictive forward empirical model. This acquired model is used on-line for manipulation planning and control as it improves. Rather than explicitly inverting the forward model to achieve... (Update)
Context of citations to this paper: More
.... the use of a memory based algorithm to rapidly learn a forward model of the effects of pushing actions can be found in a companion paper [13]. V. CONCLUSION We have demonstrated the utility of optical flow as a direct and reliable qualitative measure for control of manipulator...
...allows for stochastic inversion of the model. This approach has already been used succesfully for a pushing task with a robot arm. See [26] where it is more fully described, but briefly, the inversion problem and solution are as follows. Given the current f , O , RO , find...
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Multiple-Context Mobile Robot Control Tasks Using.. - Bogoni.. (Correct)
Report of the AAAI Fall Symposium on Machine Learning and.. - Bowyer, Hall, al. (1994) (Correct)
A Direct Approach to Vision Guided Manipulation - Salganicoff, Metta, Oddera.. (1993) (Correct)
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0.5: Stable Pushing: Mechanics, Controlability, and Planning - Lynch, Mason (Correct)
0.2: Task Level Strategies for Robots - Narasimhan (1994) (Correct)
0.2: Exploratory research in telerobotic control using ATM networks - Bajcsy, Smith (Correct)
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3: Towards autonomous learning of behavior by a robot (context) - Zrimec - 1990
3: Estimating the friction parameters of pushed objects - Lynch - 1993
2: Experiments in robot learning (context) - Mason, Christiansen et al. - 1989
BibTeX entry: (Update)
Salganicoff, M., Metta, G., Oddera, A., and Sandini, G. 1993b. A vision-based learning method for pushing manipulation. AAAI Fall Symp. on Machine Learning in Computer Vision. http://citeseer.ist.psu.edu/salganicoff93visionbased.html More
@misc{ salganicoff-visionbased,
author = "M. Salganicoff and G. Metta and A. Oddera and G. Sandini",
title = "A vision-based learning method for pushing manipulation",
text = "Salganicoff, M., Metta, G., Oddera, A., and Sandini, G. 1993b. A vision-based
learning method for pushing manipulation. AAAI Fall Symp. on Machine Learning
in Computer Vision.",
url = "citeseer.ist.psu.edu/salganicoff93visionbased.html" }
Citations (may not include all citations):755 Determining optical flow (context) - Horn, Schunk - 1981
510 Robust Statistics (context) - Huber - 1981
247 An algorithm for finding best matches in logarithmic expecte.. (context) - Friedman, Bentley et al. - 1977
76 Mechanics and planning of manipulator pushing operations (context) - Mason - 1986
49 Wiley and Sons (context) - Duda, Hart et al. - 1973
28 Acquisition of dynamic control knowledge for a robotic manip.. (context) - Moore - 1990
18 Connectionist robot motion planning: A neurally inspired app.. (context) - Mel - 1991
17 Density-adaptive learning and forgetting - Salganicoff - 1993
15 Cross-validation: a review (context) - Stone - 1977
8 Task-level robot learning - Aboaf, Atkeson et al. - 1989
7 Estimating the friction parameters of pushed objects - Lynch - 1993
7 Robotic manipulation planning with stochastic actions (context) - Christiansen, Goldberg - 1990
7 Implementation of a Robot Control Development Environment (context) - Lloyd - 1986
5 A direct approach to vision guided manipulation - Salganicoff, Metta et al. - 1993
4 A multiagent system for intelligent material handling (context) - Bajcsy, Paul et al. - 1991
4 Experiments in robot learning (context) - Mason, Christiansen et al. - 1989
4 Towards Autonomous Learning of a Behavior by a Robot (context) - Zrimec - 1990
3 A robotic haptic system architecture (context) - Campos, Bajcsy - 1990
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