Active Semantic Sensing and Planning for Human-Robot Collaboration Uncertain Environments
2020 International Conference on Robotics and Automation (Submitted)
(Submitted) May 2020
Autonomous robots can benefit greatly from human-provided semantic characterizations of uncertain task environments and states. However, the development of integrated strategies which let robots model, communicate, and act on such ‘soft data’ remains challenging. We present a framework for active semantic sensing and planning in humanrobot teams which addresses these gaps by formally combining the benefits of online sampling-based POMDP policies, multimodal semantic interaction, and Bayesian data fusion. This approach lets humans opportunistically impose model structure and dynamically extend the range of semantic soft data in uncertain environments, which otherwise yield little information to a lone robot. It also lets robots actively query humans for new semantic data which update understanding and beliefs of unknown environments for improved online planning. Dynamic target search simulations show that active collaborative semantic sensing leads to significant improvements in time and belief state estimates required for interception versus conventional planning, which relies on robotic sensing only.