isaac-sim/IsaacLab

Unified framework for robot learning built on NVIDIA Isaac Sim

Language: Python - Size: 18.5 MB - Last synced: about 3 hours ago - Pushed: about 4 hours ago - Stars: 1,079 - Forks: 319

Weixy21/BarrierNet

Safe robot learning

Language: Python - Size: 3.64 MB - Last synced: about 1 hour ago - Pushed: about 16 hours ago - Stars: 48 - Forks: 13

Aaditya-Prasad/consistency-policy

[RSS 2024] Consistency Policy: Accelerated Visuomotor Policies via Consistency Distillation

Language: Python - Size: 13.6 MB - Last synced: 4 days ago - Pushed: 4 days ago - Stars: 63 - Forks: 1

vikashplus/robohive

A unified framework for robot learning

Language: Python - Size: 65.2 MB - Last synced: 4 days ago - Pushed: about 1 month ago - Stars: 441 - Forks: 80

robocasa/robocasa

RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots

Language: Python - Size: 5.59 MB - Last synced: 6 days ago - Pushed: 7 days ago - Stars: 134 - Forks: 5

personalrobotics/CCIL

Code release and project site for "CCIL: Continuity-based Data Augmentation for Corrective Imitation Learning"

Language: Python - Size: 155 MB - Last synced: 15 days ago - Pushed: 16 days ago - Stars: 0 - Forks: 0

simpler-env/SimplerEnv

Evaluating and reproducing real-world robot manipulation policies (e.g., RT-1, RT-1-X, Octo) in simulation under common setups (e.g., Google Robot, WidowX+Bridge)

Language: Jupyter Notebook - Size: 15.5 MB - Last synced: 6 days ago - Pushed: 6 days ago - Stars: 93 - Forks: 3

clvrai/furniture-bench

FurnitureBench: Real-World Furniture Assembly Benchmark (RSS 2023)

Language: Python - Size: 170 MB - Last synced: 13 days ago - Pushed: 13 days ago - Stars: 103 - Forks: 14

lstrgar/ELDiR

Code for "Evolution and learning in differentiable robots", Strgar et al, proceedings of Robotics: Science and Systems 2024

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ARISE-Initiative/robosuite

robosuite: A Modular Simulation Framework and Benchmark for Robot Learning

Language: Python - Size: 312 MB - Last synced: 15 days ago - Pushed: 16 days ago - Stars: 1,104 - Forks: 360

wangcongrobot/awesome-isaac-gym

A curated list of awesome NVIDIA Issac Gym frameworks, papers, software, and resources

Size: 7.91 MB - Last synced: 14 days ago - Pushed: about 1 year ago - Stars: 637 - Forks: 56

notmahi/dobb-e

Dobb·E: An open-source, general framework for learning household robotic manipulation

Language: G-code - Size: 52.7 MB - Last synced: 22 days ago - Pushed: 27 days ago - Stars: 514 - Forks: 47

damianliumin/SoftMAC

Code repository for our paper SoftMAC: Differentiable Soft Body Simulation with Forecast-based Contact Model and Two-way Coupling with Articulated Rigid Bodies and Clothes

Language: Python - Size: 16.6 MB - Last synced: 22 days ago - Pushed: 4 months ago - Stars: 15 - Forks: 2

clvrai/furniture

IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks

Language: Python - Size: 671 MB - Last synced: about 1 month ago - Pushed: over 1 year ago - Stars: 483 - Forks: 56

UT-Austin-RPL/GIGA

Official PyTorch implementation of Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations

Language: Python - Size: 408 KB - Last synced: 22 days ago - Pushed: 3 months ago - Stars: 113 - Forks: 20

UT-Austin-RPL/maple

Official codebase for Manipulation Primitive-augmented reinforcement Learning (MAPLE)

Language: Python - Size: 14.7 MB - Last synced: 22 days ago - Pushed: over 1 year ago - Stars: 68 - Forks: 12

jonzamora/awesome-robot-learning-envs

A List of Popular Robot Learning Environments

Size: 99.4 MB - Last synced: about 2 months ago - Pushed: about 2 months ago - Stars: 1 - Forks: 0

UT-Austin-RPL/sirius

Official codebase for Sirius: Robot Learning on the Job

Language: Python - Size: 146 MB - Last synced: 22 days ago - Pushed: 7 months ago - Stars: 28 - Forks: 3

kindredresearch/SenseAct

SenseAct: A computational framework for developing real-world robot learning tasks

Language: Python - Size: 87 MB - Last synced: about 2 months ago - Pushed: over 1 year ago - Stars: 207 - Forks: 40

andreaprotopapa/sofa-dr-rl

Enabling Faster Training of Robust Reinforcement Learning Policies for Soft Robots

Language: Python - Size: 297 MB - Last synced: 3 months ago - Pushed: 3 months ago - Stars: 2 - Forks: 0

SoheilKhatibi/Webots-Image-Dataset-Collector

Webots Image Dataset Collection For Computer Vision And Deep Learning

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UT-Austin-RPL/BUDS

Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulation (BUDS)

Language: Python - Size: 106 KB - Last synced: 22 days ago - Pushed: over 2 years ago - Stars: 41 - Forks: 6

mikeroyal/Robotics-guide

Robotics Guide

Language: Python - Size: 212 KB - Last synced: 16 days ago - Pushed: 5 months ago - Stars: 51 - Forks: 11

42jaylonw/shifu

Lightweight Isaac Gym Environment Builder

Language: Python - Size: 31.6 MB - Last synced: 2 months ago - Pushed: over 1 year ago - Stars: 30 - Forks: 1

enfff/robot-learning-labs

Labs for the Robot Learning class, focusing on robotics and Reinforcement Learning. Each lab focuses on a different topic, had mandatory tasks and eventually extensions. All the results have been discussed in the reports.

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Improbable-AI/airobot

A python library for robot learning - An extension to PyRobot

Language: Python - Size: 67.6 MB - Last synced: 3 months ago - Pushed: 3 months ago - Stars: 67 - Forks: 23

RayYoh/Awesome-Robot-Learning

This repo contains a curative list of robot learning (mainly for manipulation) resources.

Size: 6.02 MB - Last synced: 3 months ago - Pushed: 3 months ago - Stars: 113 - Forks: 5

HariKrishnan06082k/Robot-Learning-for-Planning-and-Control

Topics include function approximation, learning dynamics, using learned dynamics in control and planning, handling uncertainty in learned models, learning from demonstration, and model-based and model-free reinforcement learning.

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rrahmati/roboinstruct-2

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

Language: C++ - Size: 1.32 MB - Last synced: 2 months ago - Pushed: over 6 years ago - Stars: 45 - Forks: 14

Lifelong-Robot-Learning/LIBERO

Benchmarking Knowledge Transfer in Lifelong Robot Learning

Language: Jupyter Notebook - Size: 308 MB - Last synced: 5 months ago - Pushed: 5 months ago - Stars: 107 - Forks: 12

mengyuest/stl_npc

[ICRA2024] A differentiable robot learning framework for task specifications and controller synthesis.

Language: Python - Size: 7.98 MB - Last synced: 6 months ago - Pushed: 6 months ago - Stars: 0 - Forks: 0

gabrieletiboni/paintnet

PaintNet: Unstructured Multi-Path Learning from 3D Point Clouds for Robotic Spray Painting

Language: Python - Size: 31.3 MB - Last synced: 9 months ago - Pushed: 9 months ago - Stars: 5 - Forks: 1

Derek-TH-Wang/hybrid_gait

RL training for quadruped robot(mit minicheetah) various gaits in different velocity based on MPC controller.

Language: C++ - Size: 16.6 MB - Last synced: 4 months ago - Pushed: almost 2 years ago - Stars: 10 - Forks: 2

andreaprotopapa/dr-soro

Domain Randomization for Robust, Affordable and Effective Closed-loop Control of Soft Robots

Size: 72.5 MB - Last synced: 4 months ago - Pushed: 4 months ago - Stars: 7 - Forks: 0

pantor/griffig

Robotic Manipulation - Learned in the Real World

Language: Python - Size: 739 KB - Last synced: 7 months ago - Pushed: over 2 years ago - Stars: 42 - Forks: 9

navigator8972/vae_assoc

Associative Variational Auto-encoders

Language: Python - Size: 966 KB - Last synced: 7 months ago - Pushed: over 4 years ago - Stars: 5 - Forks: 3

navigator8972/roboschool_baxterstriker

roboschool environment for baxter ball striking

Language: Python - Size: 8.21 MB - Last synced: 7 months ago - Pushed: over 6 years ago - Stars: 1 - Forks: 1

clvrai/spirl

Official implementation of "Accelerating Reinforcement Learning with Learned Skill Priors", Pertsch et al., CoRL 2020

Language: Python - Size: 16.6 MB - Last synced: 7 months ago - Pushed: almost 1 year ago - Stars: 165 - Forks: 32

JunfengChen-robotics/MultiRoboLearn

This is a software which can by used by researcher multi-agent reinforcement learning in robot learning for multi-robot system

Language: Python - Size: 50.6 MB - Last synced: 7 months ago - Pushed: about 3 years ago - Stars: 11 - Forks: 5

clvrai/mopa-rl

Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments (CoRL 2020)

Language: Python - Size: 251 MB - Last synced: 7 months ago - Pushed: over 1 year ago - Stars: 63 - Forks: 11

Aryia-Behroziuan/Robot-learning

In developmental robotics, robot learning algorithms generate their own sequences of learning experiences, also known as a curriculum, to cumulatively acquire new skills through self-guided exploration and social interaction with humans. These robots use guidance mechanisms such as active learning, maturation, motor synergies and imitation. Association rules Main article: Association rule learning See also: Inductive logic programming Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".[60] Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.[61] Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems. Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets.[62] For example, the rule {\displaystyle \{\mathrm {onions,potatoes} \}\Rightarrow \{\mathrm {burger} \}}\{{\mathrm {onions,potatoes}}\}\Rightarrow \{{\mathrm {burger}}\} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions. Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.[63] Inductive logic programming (ILP) is an approach to rule-learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as functional programs. Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting.[64][65][66] Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples.[67] The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set. Models Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems. Artificial neural networks Main article: Artificial neural network See also: Deep learning An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another. Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68]

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mhr/kcpo-icml

Experiment code for "Koopman Constrained Policy Optimization: a Koopman operator theoretic method for differentiable optimal control in robotics" as presented at ICML 2023

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domingoesteban/robolearn_envs

A Python package with OpenAI-Gym-like environments for Robot Learning

Language: Python - Size: 75.9 MB - Last synced: 10 months ago - Pushed: about 5 years ago - Stars: 2 - Forks: 2

domingoesteban/robolearn

A Python package for Robot Learning

Language: Jupyter Notebook - Size: 165 MB - Last synced: 10 months ago - Pushed: over 4 years ago - Stars: 1 - Forks: 1

eloukas/practical-rl

My solutions to the Practical Reinforcement Learning course by Coursera/HSE.

Language: Jupyter Notebook - Size: 17.3 MB - Last synced: 10 months ago - Pushed: over 4 years ago - Stars: 9 - Forks: 7

Hilton-AH/Imitation_Learning-Behavioral_Cloning-for-Robot-Learning

Lunar Lander game from OpenAI Gym using behavioral cloning, DAgger methods, and POMDP(Partially-Observable Markov Decision Processes)

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Hilton-AH/YODO-novel-RL-algorithm

Using DAgger with our MPC treated as the expert, we are able to effectively distill knowledge into relatively simple networks while still being able to retain a large fraction of the performance. (Please see paper for full description).

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inspirai/MetalHead

Natural Locomotion, Jumping and Recovery of Quadruped Robot A1 with AMP

Language: Python - Size: 68.1 MB - Last synced: 10 months ago - Pushed: about 1 year ago - Stars: 34 - Forks: 1

AshishMehtaIO/Quadruped-robot-DQN

Language: C - Size: 73.2 KB - Last synced: 10 months ago - Pushed: over 5 years ago - Stars: 4 - Forks: 0

med-air/ViSkill

[IROS'23] Value-Informed Skill Chaining for Policy Learning of Long-Horizon Tasks with Surgical Robot

Language: Python - Size: 333 KB - Last synced: 10 months ago - Pushed: 10 months ago - Stars: 3 - Forks: 0

paulcode123/Bidirectional-VAE

A product of my Summer 2023 Internship at NYU Courant

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ChengshuLi/HRL4IN

Code for CoRL 2019 paper: HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulators

Language: Python - Size: 83 KB - Last synced: 10 months ago - Pushed: almost 3 years ago - Stars: 41 - Forks: 14

NVlabs/oscar

Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation

Language: Python - Size: 191 MB - Last synced: 10 months ago - Pushed: about 2 years ago - Stars: 85 - Forks: 13

clvrai/skimo

Skill-based Model-based Reinforcement Learning (CoRL 2022)

Language: Python - Size: 50.2 MB - Last synced: 11 months ago - Pushed: over 1 year ago - Stars: 30 - Forks: 4

GT-RAIL/clamp

Combined Learning from Demonstration and Motion Planning

Language: MATLAB - Size: 228 KB - Last synced: 2 months ago - Pushed: over 5 years ago - Stars: 10 - Forks: 6

clvrai/skill-chaining

Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization (CoRL 2021)

Language: Python - Size: 8.81 MB - Last synced: about 1 year ago - Pushed: about 2 years ago - Stars: 18 - Forks: 2

floodsung/MetalHead Fork of inspirai/MetalHead

Natural Locomotion, Jumping and Recovery of Quadruped Robot A1 with AMP

Size: 68.1 MB - Last synced: 11 months ago - Pushed: about 1 year ago - Stars: 0 - Forks: 0

wanxinjin/Learning-from-Directional-Corrections

A new method for a robot to learn a control objective from human user's directional corrections.

Language: Python - Size: 168 KB - Last synced: about 1 year ago - Pushed: over 2 years ago - Stars: 11 - Forks: 2

yash-goel/reading-list

For Robotics and Robot Learning Resources

Size: 105 KB - Last synced: 11 months ago - Pushed: almost 5 years ago - Stars: 5 - Forks: 3

NVlabs/ACID

ACID: Action-Conditional Implicit Visual Dynamics for Deformable Object Manipulation

Language: Python - Size: 3.04 MB - Last synced: about 1 year ago - Pushed: about 2 years ago - Stars: 47 - Forks: 4

clvrai/mopa-pd

Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation (CoRL 2021)

Language: Python - Size: 19.7 MB - Last synced: 10 months ago - Pushed: over 2 years ago - Stars: 6 - Forks: 2

PietroVitiello/ActionRepresentation

MSc Project aimed at finding an alternative way of representing robot actions. We evaluate several machine learning models to control a simulated 7-joint robotic arm using solely a wrist mounted camera as input.

Language: Python - Size: 154 MB - Last synced: about 1 year ago - Pushed: over 1 year ago - Stars: 7 - Forks: 1

RobustFieldAutonomyLab/DRL_graph_exploration

Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs

Language: C++ - Size: 239 MB - Last synced: over 1 year ago - Pushed: almost 3 years ago - Stars: 75 - Forks: 13

SarthakJShetty/tfn-robot

This repository hosts the physical robot code for ToolFlowNet. Published at CoRL '22.

Language: Python - Size: 33 MB - Last synced: about 2 months ago - Pushed: about 2 months ago - Stars: 4 - Forks: 0

wangcongrobot/Robot-Learning-From-Scratch

How to train a robot to learn skills from scratch

Language: Python - Size: 4.15 MB - Last synced: about 1 year ago - Pushed: about 4 years ago - Stars: 5 - Forks: 3

RobustFieldAutonomyLab/DRL_robot_exploration

Self-Learning Exploration and Mapping for Mobile Robots via Deep Reinforcement Learning

Language: Python - Size: 278 MB - Last synced: over 1 year ago - Pushed: over 3 years ago - Stars: 32 - Forks: 11

am-shb/dqn-navigation

A deep reinforcement learning agent learns to navigate and collect rewards in a large world using lidar and camera.

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am-shb/evobots

A quadruped robot learns to control its joints to move towards an objective using genetic algorithm.

Language: Python - Size: 12.7 KB - Last synced: about 1 year ago - Pushed: over 3 years ago - Stars: 1 - Forks: 0

taochenshh/hcp

(NeurIPS 2018) Hardware Conditioned Policies for Multi-Robot Transfer Learning

Language: Python - Size: 1.19 MB - Last synced: 12 months ago - Pushed: about 5 years ago - Stars: 17 - Forks: 7

Ramtin92/mmvp

Visual Next-Frame Prediction using Multisensory Perception for Embodied Agents

Language: Python - Size: 5 MB - Last synced: over 1 year ago - Pushed: over 1 year ago - Stars: 2 - Forks: 1

Xiaohui9607/mmvp

Visual Next-Frame Prediction using Multisensory Perception for Embodied Agents

Language: Python - Size: 33.9 MB - Last synced: about 1 year ago - Pushed: over 1 year ago - Stars: 3 - Forks: 0

Xiaohui9607/physical_interaction_video_prediction_pytorch

Based on Chealsea Finn's et al "Unsupervised Learning for Physical Interaction through Video Prediction"

Language: Python - Size: 46.9 KB - Last synced: about 1 year ago - Pushed: almost 4 years ago - Stars: 26 - Forks: 4

remiMZ/remiMZ.github.io

Min Zhang's Homepage (Westlake University)

Language: Python - Size: 27.7 MB - Last synced: over 1 year ago - Pushed: over 1 year ago - Stars: 0 - Forks: 0

MAdeelArshad/picnui-reactserver

The front-end design on React for PICNUI project to train the cobot using 3d TensorFlow pose estimation robot tracking

Language: JavaScript - Size: 2.81 MB - Last synced: about 1 year ago - Pushed: over 3 years ago - Stars: 2 - Forks: 1

abakisita/r_n_d_report

Language: TeX - Size: 20 MB - Last synced: 10 months ago - Pushed: over 5 years ago - Stars: 1 - Forks: 3

bhairavmehta95/asymmetric-actor-critic

Implementation of Asymmetric Actor Critic for Image-Based Robot Learning in Tensorflow.

Language: Python - Size: 67.4 KB - Last synced: over 1 year ago - Pushed: about 5 years ago - Stars: 10 - Forks: 7

tufts-ai-robotics-group/mmvp

A Framework for Multisensory Foresight for Embodied Agents

Language: Python - Size: 2.83 MB - Last synced: over 1 year ago - Pushed: about 3 years ago - Stars: 0 - Forks: 0

zhanpenghe/SoftQLearning

Implementation of Soft QLearning Algorithm

Language: Python - Size: 7.81 KB - Last synced: over 1 year ago - Pushed: about 6 years ago - Stars: 3 - Forks: 0

ashwinreddy/rlg

Robot Learning Gym

Language: Python - Size: 27.9 MB - Last synced: 7 months ago - Pushed: over 7 years ago - Stars: 2 - Forks: 0