Robert Dyro

I am a PhD candidate in Autonomous Systems Lab (ASL) at Stanford University, where I work on modeling and optimization algorithms for robotics.

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Projects

Here are some articles I (co)authored showcasing some of the projects I worked on.

alttext Post-training Compression of Neural Network via SVD
Robert Dyro

Post-training reduction of quantized NN memory footprint via SVD.

alttext torch2jax - Call torch functions and models from JAX
Robert Dyro

A package for efficiently wrapping existing PyTorch code as JAX functions in a JIT-compatible way. AutoDiff gradients can be automatically defined, fully integrating any PyTorch code with JAX differentiation.

alttext Optimal Control for Airplane Control
Robert Dyro, Rohan Sinha

Controlling an airplane using optimal control with tuned objective parameterization using ray.tune.

alttext Automatic Grader
Robert Dyro

Automatic short answer grading with pretrained language models.

alttext GraphGym Tutorial and Neural Architecture Search
Robert Dyro, Ricky Grannis-Vu

An introduction to GraphGym and an extension of GraphGym to perform Neural Architecture Search (NAS).

alttext JAX Advanced Techniques
Robert Dyro, Spencer Richards

This tutorial is at attempt to produce an educational reference for JAX techniques we found particularly interesting in our work and research. As such, it is a collection of a few disjoint topics.

alttext Optimizing Models for Fairness and Explainability
Robert Dyro, Somrita Banerjee

Optimizing Models for Fairness and Explainability via Shapley value and local linear model penalization and differentiation.

alttext Sensitivity analysis using PyTorch and JAX
Robert Dyro, Ed Schmerling

Optimization sensitivity analysis with PyTorch and JAX.

alttext CUDA QP Solver
Robert Dyro

A QP solver fitting into a single CUDA block.

alttext Sparse Automatic Differentiation
Robert Dyro

Reverse-mode automatic differentiation with full sparse Jacobians and Hessians.

alttext Model Predictive Control
Robert Dyro, James Harrison

Model Predictive Control - general and consensus optimization in Julia & Python.

Research

I'm interested in optimization, robotics, optimization, and machine learning. My research is about creating new models and algorithms for robotic systems that exploit known structure of the problem.

alttext Learning Deep SDF Maps Online for Robot Navigation and Exploration
Gadiel Sznaier Camps, Robert Dyro, Marco Pavone, Mac Schwager
arXiv, 2022

Optimal planning & control in new environments can be done effectively by learning deep signed distance function (SDF) maps from LiDAR data.

alttext Second-Order Sensitivity Analysis for Bilevel Optimization
Robert Dyro, Edward Schmerling, Nikos Arechiga, Marco Pavone
Artificial Intelligence and Statistics (AISTATS), 2022

Differentiating through optimization can be done twice to obtain bilevel program Hessians.

alttext Particle MPC for Uncertain and Learning-Based Control
Robert Dyro, James Harrison, Apoorva Sharma, Marco Pavone
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021

Sampling arbitrary dynamical uncertainty and then optimization over the possibilities <em>jointly</em> gives safe real-time optimal control.