Perceivable-Human-Robot-Interaction-using-Neural-Attention-Q-Networks
Robot teacher
An implementation of a deep reinforcement learning algorithm to teach a robot to interact with humans in a socially acceptable manner.
Multimodal Deep Attention Recurrent Q-Network for perceivable social human-robot interaction.
4 stars
2 watching
0 forks
Language: Lua
last commit: over 9 years agoactivity-recognitionactivity-understandingattention-mechanismdeep-reinforcement-learninghuman-robot-interactionperceptionreinforcement-learningsocial-interaction-skills
Related projects:
| Repository | Description | Stars |
|---|---|---|
| A deep reinforcement learning system designed to enable robots to acquire social interaction skills through multimodal interactions with humans. | 14 | |
| An implementation of a deep reinforcement learning model for continuous control tasks | 116 | |
| Develops robot navigation policies in crowded spaces using reinforcement learning and attention mechanisms. | 607 | |
| Research on repurposing reinforcement learning for transfer between tasks in robotics and multi-step visual tasks with simulation-to-real transfer | 108 | |
| An implementation of the Q-Learning algorithm in MATLAB for training agents to navigate mazes | 43 | |
| This project implements a PyTorch-based framework for learning discrete communication protocols in multi-agent reinforcement learning environments. | 349 | |
| An implementation of a deep reinforcement learning architecture for playing Atari games | 1,828 | |
| An implementation of a deep reinforcement learning network using PyTorch to learn human-level control through trial and error. | 387 | |
| Assesses generalization of multi-agent reinforcement learning algorithms to novel social situations | 637 | |
| A deep learning framework for multi-view fusion network-based 3D human motion capture system | 52 | |
| Automates large-scale deep learning training on distributed clusters, providing fault tolerance and fast recovery from failures. | 1,302 | |
| A tool for training neural networks on Quantum Annealing (QA) optimization problems represented as QUBO matrices. | 47 | |
| Develops and analyzes convolutional neural networks for grasp quality in robotics | 316 | |
| An architecture for affective human-robot interaction using multimodal recognition and emotional response models. | 2 | |
| A PyTorch implementation of a neural network potential for molecular simulations | 471 |