ajberlier
Senior AI Research Scientist at CACI International Inc Computer Science Ph.D. Student at UMBC studying Reinforcement Learning in Human-Robot Interaction
CACI International IncDenver, CO
ajberlier's Stars
kasraprime/EMMA
EMMA: Extended Multimodal Alignment
caotians1/BabyAIPlusPlus
BabyAI++: Towards Grounded language Learning beyond Memorization, ICLR BeTR-RL 2020
krakenrf/krakensdr_docs
Documentation and Wiki for KrakenSDR
anomnaco/pygrametl_examples
Gitpod container with postgres and pygrametl examples
mklissa/PPOC
Proximal Policy Option-Critic
geospace-code/pymap3d
pure-Python (Numpy optional) 3D coordinate conversions for geospace ecef enu eci
mrdbourke/pytorch-deep-learning
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
alexxcollins/curriculum
kngwyu/Rainy
:umbrella: Deep RL agents with PyTorch:umbrella:
HobbitLong/SupContrast
PyTorch implementation of "Supervised Contrastive Learning" (and SimCLR incidentally)
aimhubio/aim
Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
petercorke/robotics-toolbox-python
Robotics Toolbox for Python
Jonas-Nicodemus/PINNs-based-MPC
We discuss nonlinear model predictive control (NMPC) for multi-body dynamics via physics-informed machine learning methods. Physics-informed neural networks (PINNs) are a promising tool to approximate (partial) differential equations. PINNs are not suited for control tasks in their original form since they are not designed to handle variable control actions or variable initial values. We thus present the idea of enhancing PINNs by adding control actions and initial conditions as additional network inputs. The high-dimensional input space is subsequently reduced via a sampling strategy and a zero-hold assumption. This strategy enables the controller design based on a PINN as an approximation of the underlying system dynamics. The additional benefit is that the sensitivities are easily computed via automatic differentiation, thus leading to efficient gradient-based algorithms. Finally, we present our results using our PINN-based MPC to solve a tracking problem for a complex mechanical system, a multi-link manipulator.
rlabbe/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.
jwyang/graph-rcnn.pytorch
[ECCV 2018] Official code for "Graph R-CNN for Scene Graph Generation"
vincentschen/limited-label-scene-graphs
Scene Graph Prediction with Limited Labels
alexacarlson/SensorEffectTransferNetwork
This is the public git repo for the paper "Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation"
rougier/scientific-visualization-book
An open access book on scientific visualization using python and matplotlib
ChristopherSweeney/RGBD_SIM
This repo contains a model and code to add realistic depth sensor dropout for RGBD camera.
gizatt/pydrake_rgbd_sim
Intelligent-Quads/iq_sim
example gazebo ardupilot simulation package
gtri/scrimmage
Multi-Agent Robotics Simulator
ColinShaw/machine-learning-resources
List of books and other resources that are related to deep learning
chendagui16/reinforcement-learning-an-introduction
Python implementation of Reinforcement Learning: An Introduction
uzh-rpg/rpg_information_field
Information Field for Perception-aware Planning
kandouss/marlgrid
Gridworld for MARL experiments
mila-iqia/teamgrid
Multiagent gridworld for the TEAM project based on gym-minigrid
mila-iqia/babyai
BabyAI platform. A testbed for training agents to understand and execute language commands.
fmof/ada-tutorial
IntelRealSense/realsense-ros
ROS Wrapper for Intel(R) RealSense(TM) Cameras