Mahadevan, S.; Theocharous, G.; and Khaleeli, N. 1998. Rapid concept learning for mobile robots. Autonomous Robots Journal 5:239--251. Home/Search Document Details and Download
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Probabilistic Planning for Behavior-Based Robots - Ro Bo Ts (Correct)
....Kaelbling, Littman 1994) So far, they have been used mainly to solve low level planning tasks Copyright c fl 2000, American Association for Artificial Intelligence (www.aaai.org) All rights reserved. for mobile robots such as path following and localization (Fox, Burgard, Thrun 1998; Mahadevan, Theocharous, Khaleeli 1998; Cassandra, Kaelbling, Kurien 1996; Simmons Koenig 1995) In this paper, we show that POMDPs can also be used to solve higher level planning tasks for mobile robots such as sensor planning. The key idea behind our robot architecture is that POMDP planners can generate policy graphs rather ....
Mahadevan, S.; Theocharous, G.; and Khaleeli, N. 1998. Rapid concept learning for mobile robots. Autonomous Robots Journal 5:239--251.
Learning a Door-Reaching Behavior Using Visual Information - Cicirelli, D'Orazio.. (2000) (Correct)
....sonar sensor, odometry and proximity sensors have been used to solve elementary behavior however limited only to local tasks. Visual sensors, instead, can be more useful since they are able to detect distant goals and permit the acquisition of suitable behaviors for more global goal directed tasks [2,7]. In our work wehave considered a goal reaching task: the working environment is a close environment without obstacles into and with a red door. The robot hastomovetowards the door from every pointinthe environmentuntil it is located adjacent to the door. The door reaching behavior has been ....
S. Mahadevan, G. Theochaous, and N. Khaleeli. Rapid concept learning for mobile robots. Machine Learning, 1998. in press.
"Virtual Coordinates": Perception-based Localisation and.. - Nehmzow, Matsui, Asoh (1998) (1 citation) (Correct)
....territory. If navigation is to be achieved over short time intervals and short distances only, odometry based systems can be used. Such systems either rely on odometry alone, or incorporate a perception based correction element, which allows the occasional calibration of a robot s wheel encoders ([5, 3]) Because of the fundamental problem of odometry wheel slippage alternative approaches have used perception based methods for navigation ( 4, 1, 6] Such methods do not suffer from accumulated sensing error, but fail if several locations in the environment appear identical to the robot s ....
S. Mahadevan, G. Theocharous and N. Khaleeli, "Rapid Concept Learning for Mobile Robots", to appear in J. Machine Learning, 1998.
Self-Localisation and Autonomous Navigation by a Mobile Robot - Duckett, Nehmzow (1999) (1 citation) (Correct)
....Two levels of pre processing are applied to the sensory input, then a neural network is used to predict the presence or absence of free space in a given direction. the training examples with the desired output categories (e.g. as in the concept learning mechanism described by Mahadevan et al. [12]) The sensing strategy used for predicting free space consisted of rotating the robot s turret to obtain a detailed scan of sonar and infrared readings. For data collection, a scan was first taken, then the robot attempted to move as far as possible in an arbitrary direction until an obstacle ....
S. Mahadevan, G. Theocharous and N. Khaleeli, Rapid Concept Learning for Mobile Robots, J. Autonomous Robots, Vol. 5, pp. 239-251, 1998.
Autonomous Environment and Task Adaptation for Robotic Agents - Beetz, Belker (Correct)
....such correct and complete domain theories and is therefore more promising for parameterization learning. MTTL can be regarded as a form of explanation based learning that can use symbolically represented domain knowledge that is not required to be complete and correct. Inductive concept learning [MTK98] can be used in combination with other learning techniques like reinforcement learning [SR97] or explanation based learning [MT93] In our own research we plan to apply inductive concept learning methods to learn part of the models used by MTTL. Goel et al. GSCR97] introduce a framework that ....
S. Mahadevan, G. Theocharous, and N. Khaleeli. Rapid concept learning for mobile robots. Autonomous Robots Journal, special Issue on Learning in Autonomous Robots, 5:239--251, 1998.
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