DARPA Mobile Autonomous Robot SoftwareLeslie Pack Kaelbling; January Adaptive Intelligent Mobile Robotics Leslie Pack Kaelbling Artificial Intelligence Laboratory MIT

DARPA Mobile Autonomous Robot SoftwareLeslie Pack Kaelbling; January Progress to Date Erik the Red Video game environment Optical flow implementation Fast bootstrapped reinforcement learning

DARPA Mobile Autonomous Robot SoftwareLeslie Pack Kaelbling; January Erik the Red RWI B21 robot camera, sonars, laser range-finder, infrareds 3 Linux machines ported our framework for writing debuggable code

DARPA Mobile Autonomous Robot SoftwareLeslie Pack Kaelbling; January Erik the Red

DARPA Mobile Autonomous Robot SoftwareLeslie Pack Kaelbling; January Crystal Space Public-domain video-game environment complex graphics other agents highly modifiable

DARPA Mobile Autonomous Robot SoftwareLeslie Pack Kaelbling; January Crystal Space

DARPA Mobile Autonomous Robot SoftwareLeslie Pack Kaelbling; January Optical Flow Get range information visually by computing optical flow field nearer objects cause flow of higher magnitude expansion pattern means you’re going to hit rate of expansion tells you when elegant control laws based on center and rate of expansion (derived from human and fly behavior)

DARPA Mobile Autonomous Robot SoftwareLeslie Pack Kaelbling; January Optical Flow in Crystal Space

DARPA Mobile Autonomous Robot SoftwareLeslie Pack Kaelbling; January Making RL Really Work Typical RL methods require far too much data to be practical in an online setting. Address the problem by strong generalization techniques using human input to bootstrap

DARPA Mobile Autonomous Robot SoftwareLeslie Pack Kaelbling; January JAQL Learning a value function in a continuous state and action space based on locally weighted regression (fancy version of nearest neighbor) algorithm knows what it knows use meta-knowledge to be conservative about dynamic-programming updates

DARPA Mobile Autonomous Robot SoftwareLeslie Pack Kaelbling; January Incorporating Human Input Humans can help a lot, even if they can’t perform the task very well. Provide some initial successful trajectories through the space Trajectories are not used for supervised learning, but to guide the reinforcement-learning methods through useful parts of the space Learn models of the dynamics of the world and of the reward structure Once learned models are good, use them to update the value function and policy as well.

DARPA Mobile Autonomous Robot SoftwareLeslie Pack Kaelbling; January Simple Experiment The “hill-car” problem in two continuous dimensions Regular RL methods take thousands of trials to learn a reasonable policy JAQL takes 11 inefficient but eventually successful trails generated by humans to get 80% performance 10 more subsequent trials generate high quality performance in the whole space

DARPA Mobile Autonomous Robot SoftwareLeslie Pack Kaelbling; January Success Percentage

DARPA Mobile Autonomous Robot SoftwareLeslie Pack Kaelbling; January Trial Length (200 max) 54-step optimum

DARPA Mobile Autonomous Robot SoftwareLeslie Pack Kaelbling; January Next Steps Implement optical-flow control algorithms on robot Apply RL techniques to tune parameters in control algorithms on robot in real time corridor following using sonar and laser obstacle avoidance using optical flow Build highly complex simulated environment Integrate planning and learning in multi-layer system