MADP uses diffusion models to generate interdependent actions for decentralized robot swarms in coverage control, trained via imitation from a clairvoyant expert and shown to generalize and outperform baselines across varying agent densities and importance densities.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 2roles
background 1polarities
unclear 1representative citing papers
Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.
citing papers explorer
-
Scalable Multi Agent Diffusion Policies for Coverage Control
MADP uses diffusion models to generate interdependent actions for decentralized robot swarms in coverage control, trained via imitation from a clairvoyant expert and shown to generalize and outperform baselines across varying agent densities and importance densities.
-
Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing
Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.