From 7bc7700c42014f608d76c85b023c0ad7fe1f5e6b Mon Sep 17 00:00:00 2001 From: Daniel Butterfield Date: Wed, 2 Oct 2024 23:26:50 -0400 Subject: [PATCH] Tuned the shrink and sizes of Abstract section --- docs/index.html | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/index.html b/docs/index.html index 44414e3..7146960 100644 --- a/docs/index.html +++ b/docs/index.html @@ -143,14 +143,14 @@

MI-HGNN: Morphology-Informed Heterogene
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- Figure 1; Visualization of our MI-HGNN for the Mini-Cheetah robot as an example. + Figure 1; Visualization of our MI-HGNN for the Mini-Cheetah robot as an example.
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Abstract

We present a Morphology-Informed Heterogeneous Graph Neural Network (MI-HGNN) for learning-based contact perception. The architecture and connectivity of the MI-HGNN are constructed from the robot morphology, in which nodes and edges are robot joints and links, respectively. By incorporating the morphology-informed constraints into a neural network, we improve a learning-based approach using model-based knowledge. We apply the proposed MI-HGNN to two contact perception problems, and conduct extensive experiments using both real-world and simulated data collected using two quadruped robots. Our experiments demonstrate the superiority of our method in terms of effectiveness, generalization ability, model efficiency, and sample efficiency. Our MI-HGNN improved the performance of a state-of-the-art model that leverages robot morphological symmetry by 8.4% with only 0.21% of its parameters. Although MI-HGNN is applied to contact perception problems for legged robots in this work, it can be seamlessly applied to other types of multi-body dynamical systems and has the potential to improve other robot learning frameworks. Our code is made publicly available at https://github.com/lunarlab-gatech/Morphology-Informed-HGNN. @@ -170,7 +170,7 @@


Figure 2; Overview of the proposed MI-HGNN for legged robot contact perception problems. -

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Our MI-HGNN is constructed from a robot kinematic structure where nodes are joints and edges are links. Proprioceptive sensor measurements acquired at each local frame are embedded into the corresponding node through a heterogeneous encoder, and fused via several layers of Message Passing. A foot decoder attached to the foot node exacts the contact information during inference.