Planning for sequential robotics tasks often requires integrated symbolic and geometric
reasoning.
TAMP algorithms typically solve these problems by performing a tree search over high-level task
sequences while checking for kinematic and dynamic feasibility.
This can be inefficient because, typically, candidate task plans resulting from the tree search
ignore geometric information.
This often leads to motion planning failures that require expensive backtracking steps to find
alternative task plans.
We propose a novel approach to TAMP called Stein Task and Motion Planning (STAMP) that relaxes
the hybrid optimization problem into a continuous domain.
This allows us to leverage gradients from differentiable physics simulation to fully optimize
discrete and continuous plan parameters for TAMP.
In particular, we solve the optimization problem using a gradient-based variational inference
algorithm called Stein Variational Gradient Descent.
This allows us to find a distribution of solutions within a single optimization run.
Furthermore, we use an off-the-shelf differentiable physics simulator that is parallelized on
the GPU to run parallelized inference over diverse plan parameters.
We demonstrate our method on a variety of problems and show that it can find multiple diverse
plans in a single optimization run while also being significantly faster than existing
approaches.