Karmesh Yadav's repositories
RRT-Plugin
A RRT* ROS plugin for Move Base using OMPL
Constrained-ILQR
Implements a Constrained Iterative LQR controller for an AV in CARLA
stable-control-representations
Code for Stable Control Representations
Deep-RL-Assignments
Code Repo for all Deep Reinforcement Learning Assignments CMU 10-703
MissileDefense
MRSD Course Game AI Assignment
rl_local_planner
A fake local planner for connecting navigation stack and RL algorithm
baselines
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
cmu-16662-robot-ctrl
Package for running the lab scripts for Robot Autonomy
continual-learning
PyTorch implementation of various methods for continual learning (XdG, EWC, online EWC, SI, LwF, DGR, DGR+distill, RtF, iCaRL).
diffusers
🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch
eai-vc
The repository for the largest and most comprehensive empirical study of visual foundation models for Embodied AI (EAI).
EvalAI-Starters
How to create a challenge on EvalAI?
gym-gazebo
A toolkit for developing and comparing reinforcement learning algorithms using ROS and Gazebo.
habitat-lab
A modular high-level library to train embodied AI agents across a variety of tasks and environments.
home-robot
Mobile manipulation research tools for roboticists
LC_NGSIM
lane change trajectories extracted from NGSIM
mjrl
Reinforcement learning algorithms for MuJoCo tasks
pcl_filter
Package for filtering out ORB-SLAM's Point-Cloud
pytorch-a2c-ppo-acktr-gail
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
pytorchrl
Deep Reinforcement Learning algorithms implemented in PyTorch
rlpyt
Reinforcement Learning in PyTorch
ros-bridge
ROS bridge for CARLA Simulator
Trajectron-plus-plus
Code accompanying the ECCV 2020 paper "Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data" by Tim Salzmann*, Boris Ivanovic*, Punarjay Chakravarty, and Marco Pavone (* denotes equal contribution).