Grace Kwak (Grace0)

Grace0

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Home Page:gracekwak.me

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ApesofWrath

Grace Kwak's starred repositories

moveit

:robot: The MoveIt motion planning framework

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ruckig

Motion Generation for Robots and Machines. Real-time. Jerk-constrained. Time-optimal.

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nuscenes-devkit

The devkit of the nuScenes dataset.

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AgentFormer

[ICCV 2021] Official PyTorch Implementation of "AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting".

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WRAP-MCU

[IEEE Wireless, RF, Amplifier Project 2023-2024] C code for STM32 MCU's

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ece-c147-project

Group project for ECE C147, taught by Prof. Jonathan Kao.

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APReL

A Library for Active Preference-based Reward Learning Algorithms

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nfn

NF-Layers for constructing neural functionals.

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scalene

Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python

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lab_onboarding_recommended_reading

This repository is a collection of papers and research material that students need to be aware of when they are getting started with research in the lab

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roboschool

DEPRECATED: Open-source software for robot simulation, integrated with OpenAI Gym.

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Gymnasium

An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)

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BAJA-All-Terrain-Vehicle-Model

This submission contains a set of atv models created with Simulink, Simscape, and Simscape Driveline. More specifically, the file contained includes BAJA ATV model with Continuously Variable Transmission(CVT).

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urban-fiesta

M21 Simscape Model

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DigiSys

Simple object to represent digital systems, controllers, filters, etc.

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ODrive

High performance motor control

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MOAT

Model 2O Awesome Teensy

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EV

GitHub repository for SMV's Electric Vehicle

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python-data-from-web

API and web scraping workshops

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Python-Machine-Learning

D-Lab's 6 hour introduction to machine learning in Python. Learn how to perform classification, regression, clustering, and do model selection using scikit-learn in Python.

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Computational-Text-Analysis-2017

An introduction to Computational Text Analysis in four 2hr sessions designed to help beginners build intuition, and to interact with workflows for natural language processing, supervised, and unsupervised approaches. Created for CTAWG in 2017 by Ben Gebre-Medhin

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Machine-Learning-in-R

Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles

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