Name
Abstract | ||
|---|---|---|
A model-free, biologically-motivated learning and control algorithm called S-learning is described as implemented in an Surveyor SRV-1 mobile robot. S-learning demonstrated learning of robotic and environmental structure sufficient to allow it to achieve its goals (finding high- or low-contrast views in its environment). No modeling information about the task or calibration information about the robot’s actuators and sensors were used in S-learning’s planning. The ability of S-learning to make movement plans was completely dependent on experience it gained as it explored. Initially it had no experience and was forced to wander randomly. With increasing exposure to the task, S-learning achieved its goals with more nearly optimal paths. The fact that this approach is model-free implies that it may be applied to many other systems, perhaps even to systems of much greater complexity. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICNC.2009.38 | ICNC (5) |
Keywords | Field | DocType |
movement plan,low-contrast view,environmental structure,calibration information,model-free learning,surveyor srv-1,mobile robot,biologically-motivated learning,greater complexity,control algorithm,modeling information,sensors,robot sensors,strips,sequence learning,mobile robots,data mining,pixel,reinforcement learning,robot kinematics,learning artificial intelligence | Robot learning,Robotic sensing,Computer science,Robot kinematics,Artificial intelligence,Robot,Sequence learning,Machine learning,Mobile robot,Actuator,Reinforcement learning | Conference |
Citations | PageRank | References |
4 | 0.44 | 6 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brandon Rohrer | 1 | 60 | 8.93 |
| Michael Bernard | 2 | 4 | 0.44 |
| J. Dan Morrow | 3 | 17 | 1.89 |
| Fred Rothganger | 4 | 262 | 19.17 |
| Patrick Xavier | 5 | 115 | 9.64 |