Efficient Dynamics Estimation With Adaptive Model Sets

Robotic systems frequently operate under changing dynamics, such as driving across varying terrain, encountering sensing and actuation faults, or navigating around humans with uncertain and changing intent. In order to operate effectively in these situations, robots must be capable of efficiently estimating these changes in order to adapt at the decision-making, planning, and control levels. Typical estimation approaches maintain a fixed set of candidate models at each time step; however, this can be computationally expensive if the number of models is large. In contrast, we propose a novel algorithm that employs an adaptive model set. We leverage the idea that the current model set must be expanded if its models no longer sufficiently explain the sensor measurements. By maintaining only a small subset of models at each time step, our algorithm improves on efficiency; at the same time, by choosing the appropriate models to keep, we avoid compromising on performance. We show that our algorithm exhibits higher efficiency in comparison to several baselines, when tested on simulated manipulation, driving, and human motion prediction tasks, as well as in hardware experiments on a 7 DOF manipulator.