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

"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

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

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

Kernelized Movement Primitives (KMP)

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

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

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

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

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

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

Implementation of the paper "Human-like Planning for Reaching in Cluttered Environments" (ICRA 2020)

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

[NeurIPS 2022] Code for Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward Environments

Inverse optimal control from incomplete trajectory observations, proposing the concept of the recovery matrix which provides further insights into objective learning process.