01 / Planning & optimization
Representations for efficient algorithms
How can a change of representation simplify an algorithm and expose more parallel computation?
Many planners make a sequence of small decisions: sample a configuration, check an edge, and decide where to search next. That structure can limit how much work runs in parallel. In tensor motion planning, we arrange candidate configurations in layers so a GPU can evaluate many possible paths together. Changing the representation changes the operations available to the planner.
Tensor planning, optimal transport, and learned trajectory priors offer different ways to organize this search. I study how these choices affect solution quality, path diversity, and the value of additional computation. Our completeness and convergence results make the conditions for these guarantees explicit. Runtime and memory measurements then help us judge whether it fits a robot's budget.
GTMP · Anytime GTMP · MPOT · MTP · MPD · CLOT · PolyStep · AAC


































