Search Results for author: Stuart Anderson

Found 6 papers, 1 papers with code

Theseus: A Library for Differentiable Nonlinear Optimization

1 code implementation19 Jul 2022 Luis Pineda, Taosha Fan, Maurizio Monge, Shobha Venkataraman, Paloma Sodhi, Ricky T. Q. Chen, Joseph Ortiz, Daniel DeTone, Austin Wang, Stuart Anderson, Jing Dong, Brandon Amos, Mustafa Mukadam

We present Theseus, an efficient application-agnostic open source library for differentiable nonlinear least squares (DNLS) optimization built on PyTorch, providing a common framework for end-to-end structured learning in robotics and vision.

Vision Checklist: Towards Testable Error Analysis of Image Models to Help System Designers Interrogate Model Capabilities

no code implementations27 Jan 2022 Xin Du, Benedicte Legastelois, Bhargavi Ganesh, Ajitha Rajan, Hana Chockler, Vaishak Belle, Stuart Anderson, Subramanian Ramamoorthy

Robustness evaluations like our checklist will be crucial in future safety evaluations of visual perception modules, and be useful for a wide range of stakeholders including designers, deployers, and regulators involved in the certification of these systems.

Autonomous Driving

Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots

no code implementations29 Sep 2021 Tanmay Shankar, Yixin Lin, Aravind Rajeswaran, Vikash Kumar, Stuart Anderson, Jean Oh

In this paper, we explore how we can endow robots with the ability to learn correspondences between their own skills, and those of morphologically different robots in different domains, in an entirely unsupervised manner.

Translation Unsupervised Machine Translation

Learning Tactile Models for Factor Graph-based Estimation

no code implementations7 Dec 2020 Paloma Sodhi, Michael Kaess, Mustafa Mukadam, Stuart Anderson

In order to incorporate tactile measurements in the graph, we need local observation models that can map high-dimensional tactile images onto a low-dimensional state space.

Object Tracking

Towards high-throughput 3D insect capture for species discovery and diagnostics

no code implementations7 Sep 2017 Chuong Nguyen, Matt Adcock, Stuart Anderson, David Lovell, Nicole Fisher, John La Salle

Digitisation of natural history collections not only preserves precious information about biological diversity, it also enables us to share, analyse, annotate and compare specimens to gain new insights.

Mixed Reality

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