  Certain forms of uncertainty that robotic systems encounter can be explicitly
learned within the context of a known model, like parametric model
uncertainties such as mass and moments of inertia. Quantifying such parametric
uncertainty is important for more accurate prediction of the system behavior,
leading to safe and precise task execution. In tandem, providing a form of
robustness guarantee against prevailing uncertainty levels like environmental
disturbances and current model knowledge is also desirable. To that end, the
authors' previously proposed RATTLE algorithm, a framework for online
information-aware motion planning, is outlined and extended to enhance its
applicability to real robotic systems. RATTLE provides a clear tradeoff between
information-seeking motion and traditional goal-achieving motion and features
online-updateable models. Additionally, online-updateable low level control
robustness guarantees and a new method for automatic adjustment of information
content down to a specified estimation precision is proposed. Results of
extensive experimentation in microgravity using the Astrobee robots aboard the
International Space Station and practical implementation details are presented,
demonstrating RATTLE's capabilities for real-time, robust, online-updateable,
and model information-seeking motion planning capabilities under parametric
uncertainty.
