  In this work, we present a novel algorithm to perform spill-free handling of
open-top liquid-filled containers that operates in cluttered environments. By
allowing liquid-filled containers to be tilted at higher angles and enabling
motion along all axes of end-effector orientation, our work extends the
reachable space and enhances maneuverability around obstacles, broadening the
range of feasible scenarios. Our key contributions include: i) generating
spill-free paths through the use of RRT* with an informed sampler that
leverages container properties to avoid spill-inducing states (such as an
upside-down container), ii) parameterizing the resulting path to generate
spill-free trajectories through the implementation of a time parameterization
algorithm, coupled with a transformer-based machine-learning model capable of
classifying trajectories as spill-free or not. We validate our approach in
real-world, obstacle-rich task settings using containers of various shapes and
fill levels and demonstrate an extended solution space that is at least 3x
larger than an existing approach.
