YaojieBao

ancm

Codes for designing adaptive Neural Contraction Metrics (aNCMs)

Awesome_Quadrupedal_Robots

Awesome Quadrupedal Robots

bayesian-temperature-scaling

Code for "Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference" (NeurIPS Bayesian Deep Learning Workshop)

CaliCO

Code for ICCV2021 paper: Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images

cvstem

Codes for designing ConVex Optimization-based Stochastic steady-state Tracking Error Minimization (CV-STEM) Control

fairseq

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

h-baselines

A repository of high-performing hierarchical reinforcement learning models and algorithms.

Invariant_kalman_fillter

A python implementation of right invariant kalman fillter; The case includes legged robots with IMU and contact sensors

Kalman-and-Bayesian-Filters-in-Python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

lagrangian_nns

Lagrangian Neural Networks

lambda-arch

Applying the Lambda Architecture with Spark, Kafka, and Cassandra.

legged-kinematics-calibration

legged_gym

Isaac Gym Environments for Legged Robots

mfrl

Mean Field Multi-Agent Reinforcement Learning

mtmfrl

Multi Type Mean Field Reinforcement Learning

multi-stage-dnn

n2nmn

Code release for Hu et al. Learning to Reason: End-to-End Module Networks for Visual Question Answering. in ICCV, 2017

Neural-Lyapunov-Control

Learning Lyapunov functions and control policies of nonlinear dynamical systems

nn-mpc

Control simulation of a mass-spring-damper system using a model-based reinforcement learning algorithm

orbit

Unified framework for robot learning built on NVIDIA Isaac Sim

PINNs

Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

PortHamiltonianNN

Port-Hamiltonian Approach to Neural Network Training

ProbabilisticContactEstimation

Probabilistic Contact State Estimation for Legged Robots in ROS

RobustRNN

SEBR

Robust Bayesian Neural Networks by Spectral Expectation Bound Regularization

stable-baselines

A fork of OpenAI Baselines, implementations of reinforcement learning algorithms

Training-robust-neural-networks-using-Lipschitz-bounds

Here you can find the code for the paper "Training robust neural networks using Lipschitz bounds"

variant_impedance_tracking_RL

VCML

PyTorch implementation of paper "Visual Concept-Metaconcept Learner", NeruIPS 2019

walk-these-ways

Sim-to-real RL training and deployment tools for the Unitree Go1 robot.