Planning and control
HTN planning: complexity and expressivity
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Learning situation-dependent costs: improving planning from probabilistic robot execution
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Map learning and high-speed navigation in RHINO
Artificial intelligence and mobile robots
Structured reactive controllers: controlling robots that perform everyday activity
Proceedings of the third annual conference on Autonomous Agents
Multiagent Mission Specification and Execution
Autonomous Robots
Towards Real-Time Execution of Motion Tasks
The 2nd International Symposium on Experimental Robotics II
Learning how to combine sensory-motor functions into a robust behavior
Artificial Intelligence
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We are proposing here an approach and a system, called robel, that enables a designer to specify and build a robot supervision system which learns from experience very robust ways of performing a task such as "navigate to". The designer specifies a collection of Hierarchical Tasks Networks (HTN) that are complex plans, called modalities, whose primitives are sensory-motor functions. Each modality is a possible combination these functions for achieving the task. The relationship between supervision states and the appropriate modality for pursuing a task is learned through experience as a Markov Decision Process (MDP) which provides a general policy for the task. This MDP is independent of the environment; it characterizes the robot abilities for the task.