Adaptive learning systems that generate control programs for robots with varying capabilities is of importance in the implementation of autonomous robots. Learning done continuously with the best possible control program running the robot (anytime learning) can achieve the adaptability desired when implemented using some form of evolutionary computation. The difficulty with this method is that ...

Abstmcl-Reinforcement learning is an adaptive and flexible control method for autonomous system. In our previous works, we had proposed a reinforcement learning algorithm for redundant systems: "Q-learning with dynamic structuring of exploration space based on GA (QDSEGAY, and applied it to multi-agent systems. However previous works of the QDSEGA have been restricted to homogeneous agents. In ...

Autonomous helicopter flight is widely regarded to be a highly challenging control problem. Despite this fact, human experts can reliably fly helicopters through a wide range of maneuvers, including aerobatic maneuvers at the edge of the helicopter’s capabilities. We present apprenticeship learning algorithms, which leverage expert demonstrations to efficiently learn good controllers for tasks ...

The success of Autonomous Vehicle (AV) systems largely depends on fast alignment of their resources, to meet (and exceed) the actual (and perceived) requirements of the changing operational environment. A review of key literature applicable to AV practices and planning issues provides a short assessment of planning scenarios, highlighting their complexity, limitations and the possible scope for...

DESCRIPTION. Robotics and Autonomous Systems will carry articles describing fundamental developments in the field of robotics, with special emphasis on autonomous systems. An important goal of this journal is to extend the state of the art in both symbolic and sensory based robot control and learning in the context of autonomous systems. Robotics and Autonomous Systems will carry articles on th...

This work proposes an affordable setup for the application of machine learning to autonomous driving. We discuss the hardware details of the platform, and propose a scenario wherein learning from demonstration is leveraged to teach a vehicle how to drive. Preliminary results are discussed, together with potential targets enabled by the setup.