Subgoal Diffuser: Coarse-to-fine Subgoal Generation to Guide Model Predictive Control for Robot Manipulation
arxiv(2024)
摘要
Manipulation of articulated and deformable objects can be difficult due to
their compliant and under-actuated nature. Unexpected disturbances can cause
the object to deviate from a predicted state, making it necessary to use
Model-Predictive Control (MPC) methods to plan motion. However, these methods
need a short planning horizon to be practical. Thus, MPC is ill-suited for
long-horizon manipulation tasks due to local minima. In this paper, we present
a diffusion-based method that guides an MPC method to accomplish long-horizon
manipulation tasks by dynamically specifying sequences of subgoals for the MPC
to follow. Our method, called Subgoal Diffuser, generates subgoals in a
coarse-to-fine manner, producing sparse subgoals when the task is easily
accomplished by MPC and more dense subgoals when the MPC method needs more
guidance. The density of subgoals is determined dynamically based on a learned
estimate of reachability, and subgoals are distributed to focus on challenging
parts of the task. We evaluate our method on two robot manipulation tasks and
find it improves the planning performance of an MPC method, and also
outperforms prior diffusion-based methods.
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