From f8ddb6e0c0805784c79ec6fce723150e6a9f31d8 Mon Sep 17 00:00:00 2001 From: Jana Date: Mon, 4 Dec 2023 14:29:40 -0500 Subject: [PATCH] Add new links --- projects/stein-bp/index.html | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/projects/stein-bp/index.html b/projects/stein-bp/index.html index edf8242..5f6099f 100644 --- a/projects/stein-bp/index.html +++ b/projects/stein-bp/index.html @@ -82,7 +82,7 @@

Fabio Ramos

to the class "abstract" below.-->

- Decentralized control for multi-robot systems involves planning in complex, high-dimensional spaces. The planning problem is particularly challenging in the presence of potential collisions between robots and obstacles, and different sources of uncertainty such as inaccurate dynamic models and sensor noise. A multi-robot system can be represented as a graphical model, in which nodes represent individual robots and edges represent communication between robots. This representation enables the use of graphical inference algorithms for solving multi-robot control. In this short paper, we introduce Stein Variational Belief Propogation (SVBP), a novel algorithm for performing inference over the marginal distributions of nodes in a graph. We present simulation results which demonstrate that our method can represent complex, multi-modal distributions in localization and control tasks. + Decentralized coordination for multi-robot systems involves planning in challenging, high-dimensional spaces. The planning problem is particularly challenging in the presence of obstacles and different sources of uncertainty such as inaccurate dynamic models and sensor noise. In this paper, we introduce Stein Variational Belief Propagation (SVBP), a novel algorithm for performing inference over nonparametric marginal distributions of nodes in a graph. We apply SVBP to multi-robot coordination by modelling a robot swarm as a graphical model and performing inference for each robot. We demonstrate our algorithm on a simulated multi-robot perception task, and on a multi-robot planning task within a Model-Predictive Control (MPC) framework, on both simulated and real-world mobile robots. Our experiments show that SVBP represents multi-modal distributions better than sampling-based or Gaussian baselines, resulting in improved performance on perception and planning tasks. Furthermore, we show that SVBP's ability to represent diverse trajectories for decentralized multi-robot planning makes it less prone to deadlock scenarios than leading baselines.

@@ -91,6 +91,8 @@

Fabio Ramos

class "paperlinks" below. Include whatever links are relevant.-->

+   Preprint   +   Video     Workshop Paper