Implementation of Inverse Reinforcement Learning (IRL) algorithms in Python/Tensorflow. Deep MaxEnt, MaxEnt, LPIRL

🤖 The Full Process Python Package for Robot Learning from Demonstration and Robot Manipulation

Implementation of Inverse Reinforcement Learning Algorithm on a toy car in a 2D world problem, (Apprenticeship Learning via Inverse Reinforcement Learning Abbeel & Ng, 2004)

Implementation of the paper "Overcoming Exploration in Reinforcement Learning with Demonstrations" Nair et al. over the HER baselines from OpenAI

Assetto Corsa OpenAI Gym Environment

"Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task Transfer; and “Good Robot!”: Efficient Reinforcement Learning for Multi-Step Visual Tasks with Sim to Real Transfer

Integrating learning and task planning for robots with Keras, including simulation, real robot, and multiple dataset support.

A robot learning from demonstration framework that trains a recurrent neural network for autonomous task execution

Kernelized Movement Primitives (KMP)

Human Demo Videos to Robot Action Plans

Train a robot to see the environment and autonomously perform different tasks

Dynamic Motion Primitives

Code for the paper Continual Learning from Demonstration of Robotic Skills

[CVPR 2025] Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning

An implementation of Deep Q-Learning from Demonstrations (DQfD) for playing Atari 2600 video games

[ICLR 2022 Spotlight] Code for Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration

REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer (ICML 2022 Long Oral)

[ICRA 2024] Learning from Human Guidance: Uncertainty-aware deep reinforcement learning for autonomous driving.

Online Signal Temporal Logic (STL) Monte-Carlo Tree Search for Guided Imitation Learning

A framework and method to jointly learn a (neural) control objective function and a time-warping function only from sparse demonstrations or waypoints.