Topic: robot-learning Goto Github
Some thing interesting about robot-learning
Some thing interesting about robot-learning
robot-learning,Lightweight Isaac Gym Environment Builder
User: 42jaylonw
robot-learning,Domain Randomization for Robust, Affordable and Effective Closed-loop Control of Soft Robots
User: andreaprotopapa
Home Page: https://andreaprotopapa.github.io/dr-soro/
robot-learning,Enabling Faster Training of Robust Reinforcement Learning Policies for Soft Robots
User: andreaprotopapa
Home Page: https://andreaprotopapa.github.io/dr-soro/
robot-learning,robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
Organization: arise-initiative
Home Page: https://robosuite.ai
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]
User: aryia-behroziuan
robot-learning,Implementation of Asymmetric Actor Critic for Image-Based Robot Learning in Tensorflow.
User: bhairavmehta95
Home Page: https://arxiv.org/abs/1710.06542
robot-learning,Code for CoRL 2019 paper: HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulators
User: chengshuli
Home Page: https://sites.google.com/view/hrl4in
robot-learning,IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks
Organization: clvrai
Home Page: https://clvrai.com/furniture
robot-learning,FurnitureBench: Real-World Furniture Assembly Benchmark (RSS 2023)
Organization: clvrai
Home Page: https://clvrai.com/furniture-bench/
robot-learning,Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation (CoRL 2021)
Organization: clvrai
Home Page: https://clvrai.com/mopa-pd
robot-learning,Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments (CoRL 2020)
Organization: clvrai
Home Page: https://clvrai.com/mopa-rl
robot-learning,Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization (CoRL 2021)
Organization: clvrai
Home Page: https://clvrai.com/skill-chaining
robot-learning,Skill-based Model-based Reinforcement Learning (CoRL 2022)
Organization: clvrai
Home Page: https://clvrai.com/skimo
robot-learning,Official implementation of "Accelerating Reinforcement Learning with Learned Skill Priors", Pertsch et al., CoRL 2020
Organization: clvrai
robot-learning,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
User: damianliumin
Home Page: https://sites.google.com/view/softmac
robot-learning,RL training for quadruped robot(mit minicheetah) various gaits in different velocity based on MPC controller.
User: derek-th-wang
robot-learning,My solutions to the Practical Reinforcement Learning course by Coursera/HSE.
User: eloukas
robot-learning,PaintNet: Unstructured Multi-Path Learning from 3D Point Clouds for Robotic Spray Painting
User: gabrieletiboni
Home Page: https://gabrieletiboni.github.io/paintnet/
robot-learning,Combined Learning from Demonstration and Motion Planning
Organization: gt-rail
robot-learning,A python library for robot learning - An extension to PyRobot
Organization: improbable-ai
Home Page: https://airobot.readthedocs.io/
robot-learning,Natural Locomotion, Jumping and Recovery of Quadruped Robot A1 with AMP
Organization: inspirai
robot-learning,This is a software which can by used by researcher multi-agent reinforcement learning in robot learning for multi-robot system
User: junfengchen-robotics
robot-learning,SenseAct: A computational framework for developing real-world robot learning tasks
Organization: kindredresearch
Home Page: https://www.kindred.ai/SenseAct
robot-learning,Benchmarking Knowledge Transfer in Lifelong Robot Learning
Organization: lifelong-robot-learning
robot-learning,[IROS'23] Value-Informed Skill Chaining for Policy Learning of Long-Horizon Tasks with Surgical Robot
Organization: med-air
robot-learning,Robotics Guide
User: mikeroyal
robot-learning,Associative Variational Auto-encoders
User: navigator8972
robot-learning,Dobb·E: An open-source, general framework for learning household robotic manipulation
User: notmahi
Home Page: https://dobb-e.com
robot-learning,Unified framework for robot learning built on NVIDIA Isaac Sim
Organization: nvidia-omniverse
Home Page: https://isaac-orbit.github.io/orbit/
robot-learning,ACID: Action-Conditional Implicit Visual Dynamics for Deformable Object Manipulation
Organization: nvlabs
Home Page: https://b0ku1.github.io/acid/
robot-learning,Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation
Organization: nvlabs
Home Page: https://cremebrule.github.io/oscar-web
robot-learning,Robotic Manipulation - Learned in the Real World
User: pantor
robot-learning,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.
User: pietrovitiello
robot-learning,This repo contains a curative list of robot learning (mainly for manipulation) resources.
User: rayyoh
robot-learning,Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs
Organization: robustfieldautonomylab
robot-learning,Self-Learning Exploration and Mapping for Mobile Robots via Deep Reinforcement Learning
Organization: robustfieldautonomylab
robot-learning,Train a robot to see the environment and autonomously perform different tasks
User: rrahmati
robot-learning,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)
Organization: simpler-env
Home Page: https://simpler-env.github.io/
robot-learning,(NeurIPS 2018) Hardware Conditioned Policies for Multi-Robot Transfer Learning
User: taochenshh
robot-learning,Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulation (BUDS)
Organization: ut-austin-rpl
robot-learning,Official PyTorch implementation of Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations
Organization: ut-austin-rpl
robot-learning,Official codebase for Manipulation Primitive-augmented reinforcement Learning (MAPLE)
Organization: ut-austin-rpl
robot-learning,Official codebase for Sirius: Robot Learning on the Job
Organization: ut-austin-rpl
Home Page: https://ut-austin-rpl.github.io/sirius/
robot-learning,A unified framework for robot learning
User: vikashplus
Home Page: https://sites.google.com/view/robohive
robot-learning,A curated list of awesome NVIDIA Issac Gym frameworks, papers, software, and resources
User: wangcongrobot
robot-learning,How to train a robot to learn skills from scratch
User: wangcongrobot
robot-learning,A new method for a robot to learn a control objective from human user's directional corrections.
User: wanxinjin
Home Page: https://wanxinjin.github.io/posts/lfdc
robot-learning,Safe robot learning
User: weixy21
robot-learning,Based on Chealsea Finn's et al "Unsupervised Learning for Physical Interaction through Video Prediction"
User: xiaohui9607
robot-learning,For Robotics and Robot Learning Resources
User: yash-goel
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