Multi-modal Predicate Identification using Dynamically Learned Robot Controllers
Multi-modal Predicate Identification using Dynamically Learned Robot Controllers
Saeid Amiri, Suhua Wei, Shiqi Zhang, Jivko Sinapov, Jesse Thomason, Peter Stone
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 4638-4645.
https://doi.org/10.24963/ijcai.2018/645
Intelligent robots frequently need to explore the objects in their working environments. Modern sensors have enabled robots to learn object properties via perception of multiple modalities. However, object exploration in the real world poses a challenging trade-off between information gains and exploration action costs. Mixed observability Markov decision process (MOMDP) is a framework for planning under uncertainty, while accounting for both fully and partially observable components of the state. Robot perception frequently has to face such mixed observability. This work enables a robot equipped with an arm to dynamically construct query-oriented MOMDPs for multi-modal predicate identification (MPI) of objects. The robot's behavioral policy is learned from two datasets collected using real robots. Our approach enables a robot to explore object properties in a way that is significantly faster while improving accuracies in comparison to existing methods that rely on hand-coded exploration strategies.
Keywords:
Planning and Scheduling: POMDPs
Robotics: Developmental Robotics
Planning and Scheduling: Robot Planning
Robotics: Learning in Robotics