Active Reward Learning for Co-Robotic Vision Based Exploration in Bandwidth Limited Environments
This addresses the challenge of efficient co-robotic exploration for scientific data collection when communication with human operators is limited, representing an incremental improvement in active learning methods for robotics.
The paper tackles the problem of autonomous robotic visual exploration in bandwidth-limited environments by formulating a novel POMDP and introducing an active reward learning strategy based on minimizing path regret. The result shows this approach enables the robot to collect up to 17% more reward per mission than the next-best criterion in some environments.
We present a novel POMDP problem formulation for a robot that must autonomously decide where to go to collect new and scientifically relevant images given a limited ability to communicate with its human operator. From this formulation we derive constraints and design principles for the observation model, reward model, and communication strategy of such a robot, exploring techniques to deal with the very high-dimensional observation space and scarcity of relevant training data. We introduce a novel active reward learning strategy based on making queries to help the robot minimize path "regret" online, and evaluate it for suitability in autonomous visual exploration through simulations. We demonstrate that, in some bandwidth-limited environments, this novel regret-based criterion enables the robotic explorer to collect up to 17% more reward per mission than the next-best criterion.