Learning concepts from sensor data of a mobile robot

Klingspor, Volker; Morik, Katharina; Rieger, Anke D.

doi:10.1007/bf00117448

Cited by 25 publications

(17 citation statements)

References 13 publications

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“…Our approach uses a representation that is close to the real inputs and outputs of system, with that intermediate type of planning between high-level and reactive planning. Other integrated planning and learning systems for robotic tasks are [Bennet and DeJong, 1996] and [Klingspor et al, 1996]. The first one deals with the concept of permissiveness, that defines qualitative behavior for the operators.…”

Section: Related Workmentioning

confidence: 99%

Learning by Knowledge Sharing in Autonomous Intelligent Systems

García-Martínez

Borrajo

Maceri

et al. 2006

Advances in Artificial Intelligence - IBERAMIA-SBIA 2006

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Abstract. Very few learning systems applied to problem solving have focused on learning operator definitions from the interaction with a completely unknown environment. In order to achieve better learning convergence, several agents that learn separately are allowed to interchange each learned set of planning operators. Learning is achieved by establishing plans, executing those plans in the environment, analyzing the results of the execution, and combining new evidence with prior evidence. Operators are generated incrementally by combining rote learning, induction, and a variant of reinforcement learning. The results show how allowing the communication among individual learning (and planning) agents provides a much better percentage of successful plans, plus an improved convergence rate than the individual agents alone.

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Section: Related Workmentioning

confidence: 99%

Learning by Knowledge Sharing in Autonomous Intelligent Systems

García-Martínez

Borrajo

Maceri

et al. 2006

Advances in Artificial Intelligence - IBERAMIA-SBIA 2006

View full text Add to dashboard Cite

show abstract

“…In order to tie sensing and action together, Klingspor et al (1996) learn sensory and action concepts directly from the sonar data of a robot, after the data is segmented and categorized by hand. By utilizing sensor information related to actions (such as wheel encoder data), we can determine the usual action performed in each space class in an unsupervised way and use these actions as a first-attempt control policy.…”

Section: Related Workmentioning

confidence: 99%

Discovering natural kinds of robot sensory experiences in unstructured environments

Grollman

Jenkins

Wood

2006

Journal of Field Robotics

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We derive categories directly from robot sensor data to address the symbol grounding problem. Unlike model-based approaches where human intuitive correspondences are sought between sensor readings and facets of an environment (corners, doors, etc.), our method learns intrinsic categories (or natural kinds) from the raw data itself. We approximate a manifold underlying sensor data using Isomap nonlinear dimension reduction and use Bayesian clustering (Gaussian mixture models) with model identification techniques to discover kinds. Applying our technique to sensor data of different modalities and from different physical spaces we demonstrate robustness with respect to noise and robot location. We also demonstrate a method for applying learned kinds to new sensor data (out-of-sample readings) in real time to show the efficacy of our technique as a foundation for topological mapping and autonomous control. Lastly, we discuss the application of our technique toward massive (250,000 datapoint) data sets.

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“…-Other systems for robotic tasks are (Bennet and DeJong, 1996) and (Klingspor et al, 1996). The f rst one deals with the concept of permissiveness, that def nes qualitative behavior for the operators.…”

Section: Related Workmentioning

confidence: 99%

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García-Martínez

Borrajo

2000

Journal of Intelligent and Robotic Systems

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Abstract. Agents (hardware or software) that act autonomously in an environment have to be able to integrate three basic behaviors: planning, execution, and learning. This integration is mandatory when the agent has no knowledge about how its actions can affect the environment, how the environment reacts to its actions, or, when the agent does not receive as an explicit input, the goals it must achieve. Without an "a priori" theory, autonomous agents should be able to self-propose goals, set-up plans for achieving the goals according to previously learned models of the agent and the environment, and learn those models from past experiences of successful and failed executions of plans. Planning involves selecting a goal to reach and computing a set of actions that will allow the autonomous agent to achieve the goal. Execution deals with the interaction with the environment by application of planned actions, observation of resulting perceptions, and control of successful achievement of the goals. Learning is needed to predict the reactions of the environment to the agent actions, thus guiding the agent to achieve its goals more effi iently.In this context, most of the learning systems applied to problem solving have been used to learn control knowledge for guiding the search for a plan, but few systems have focused on the acquisition of planning operator descriptions. As an example, currently, one of the most used techniques for the integration of (a way of) planning, execution, and learning is reinforcement learning. However, they usually do not consider the representation of action descriptions, so they cannot reason in terms of goals and ways of achieving those goals.In this paper, we present an integrated architecture, LOPE, that learns operator def nitions, plans using those operators, and executes the plans for modifying the acquired operators. The resulting system is domain-independent, and we have performed experiments in a robotic framework. The results clearly show that the integrated planning, learning, and executing system outperforms the basic planner in that domain.

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Learning concepts from sensor data of a mobile robot

Cited by 25 publications

References 13 publications

Learning by Knowledge Sharing in Autonomous Intelligent Systems

Learning by Knowledge Sharing in Autonomous Intelligent Systems

Discovering natural kinds of robot sensory experiences in unstructured environments

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