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Yixiao Li's Projects

arxiv-style icon arxiv-style

A Latex style and template for paper preprints (based on NIPS style)

bis icon bis

Benchmark on interactive safety

dnc icon dnc

Code for "Divide-and-Conquer Reinforcement Learning"

frankx icon frankx

High-Level Motion Library for Collaborative Robots

ggcnn icon ggcnn

Generative Grasping CNN from "Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach" (RSS 2018)

gr-cnn icon gr-cnn

Antipodal Robotic Grasping using GR-ConvNet. IROS 2020.

ilqr icon ilqr

Iterative Linear Quadratic Regulator with auto-differentiatiable dynamics models

kalman-cpp icon kalman-cpp

Basic Kalman filter implementation in C++ using Eigen

learning3d icon learning3d

This is a complete package of recent deep learning methods for 3D point clouds in pytorch (with pretrained models).

realtime_urdf_filter icon realtime_urdf_filter

ROS package that can filter geometry defined in URDF models from Kinect depth images. Can also preprocess data for the OpenNI tracker, to remove backgrounds, robots etc.

research-proposal-template icon research-proposal-template

LaTeX template to outline and draft academic papers (or theses) in Computer Science (in English and German language)

safesetalgorithm icon safesetalgorithm

Various interactive simulation platforms with the safe set algorithm (SSA).

safety-gym icon safety-gym

Tools for accelerating safe exploration research.

segment-anything icon segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

siren icon siren

Official implementation of "Implicit Neural Representations with Periodic Activation Functions"

storm icon storm

Stochastic Tensor Optimization for Robot Motion - A GPU Robot Motion Toolkit

visual-pushing-grasping icon visual-pushing-grasping

Train robotic agents to learn to plan pushing and grasping actions for manipulation with deep reinforcement learning.

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