Planning and control
A multivalued logic approach to integrating planning and control
Artificial Intelligence - Special volume on planning and scheduling
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
RL-TOPS: An Architecture for Modularity and Re-Use in Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Reliable Multi-robot Coordination Using Minimal Communication and Neural Prediction
Revised Papers from the International Seminar on Advances in Plan-Based Control of Robotic Agents,
Era: learning planner knowledge in complex, continuous and noisy environments
Era: learning planner knowledge in complex, continuous and noisy environments
Frequency space representation and transitions of quadruped robot gaits
ACSC '04 Proceedings of the 27th Australasian conference on Computer science - Volume 26
Action awareness: enabling agents to optimize, transform, and coordinate plans
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Cognitive Technical Systems -- What Is the Role of Artificial Intelligence?
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Learning Behaviors Models for Robot Execution Control
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Refining the execution of abstract actions with learned action models
Journal of Artificial Intelligence Research
Realtime execution of automated plans using evolutionary robotics
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
GrAM: reasoning with grounded action models by combining knowledge representation and data mining
Proceedings of the 2006 international conference on Towards affordance-based robot control
Learning the behavior model of a robot
Autonomous Robots
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Many plan-based autonomous robot controllers generate chains of abstract actions in order to achieve complex, dynamically changing, and possibly interacting goals. The execution of these action chains often results in robot behavior that shows abrupt transitions between subsequent actions, causing suboptimal performance. The resulting motion patterns are so characteristic for robots that people imitating robotic behavior will do so by making abrupt movements between actions. In this paper we propose a novel computation model for the execution of abstract action chains. In this computation model a robot first learns situation-specific performance models of abstract actions. It then uses these models to automatically specialize the abstract actions for their execution in a given action chain. This specialization results in refined chains that are optimized for performance. As a side effect this behavior optimization also appears to produce action chains with seamless transitions between actions.