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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Google DeepMind to Build Intelligent Helper Robots

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 05 Jan, 2024
3 min read

Google DeepMind’s robotics team is making significant strides in the field of advanced robotics with the introduction of three groundbreaking AI systems—AutoRT, SARA-RT, and RT-Trajectory. These systems leverage large language models to enhance the development of versatile robots for everyday use. In this article, we delve into the capabilities of each system and their potential impact on the future of robotics.

Also Read: Google and Stanford Develop AI Housemaid

AutoRT: Scaling Robotic Learning for Real-World Applications

AutoRT is a cutting-edge AI training system. It utilizes large foundation models critical for creating robots with a deep understanding of practical human goals. By collecting diverse experiential training data, AutoRT aims to scale robotic learning, preparing robots for real-world scenarios. The system combines Visual Language Models (VLM) and Large Language Models (LLM) with robot control models (RT-1 or RT-2), orchestrating up to 20 robots simultaneously in various environments. In extensive real-world evaluations, AutoRT safely conducted 77,000 robotic trials across 6,650 unique tasks, showcasing its potential for large-scale data collection.

Google DeepMind AutoRT

SARA-RT: Making Robotics Transformers Leaner and Faster

The Self-Adaptive Robust Attention for Robotics Transformers (SARA-RT) system is a breakthrough in improving the efficiency of Robotics Transformer models. By applying an innovative “up-training” fine-tuning method, SARA-RT achieves a 10.6% accuracy boost and a 14% increase in decision-making speed for the best SARA-RT-2 models. This represents the first scalable attention mechanism providing computational improvements without sacrificing quality. SARA-RT’s adaptability to various Transformer models, including Point Cloud Transformers, demonstrates its broad applicability in the robotics industry.

Google DeepMind SARA-RT | robotics

RT-Trajectory: Enhancing Robot Motion Generalization

RT-Trajectory introduces visual contours to robot motion descriptions in training videos, enabling robots to generalize skills to new tasks effectively. By overlaying 2D trajectory sketches on training videos, RT-Trajectory provides low-level visual cues for the model as it learns robot control policies. In tests on 41 unseen tasks, an arm controlled by RT-Trajectory demonstrated a remarkable 63% task success rate. This is double the performance of existing RT models. The system’s versatility allows it to generate trajectories from human demonstrations or hand-drawn sketches. This makes it adaptable to different robot platforms.

Google DeepMind RT-Trajectory | robotics

Shaping the Future of Advanced Robotics

Google DeepMind’s advancements in AutoRT, SARA-RT, and RT-Trajectory mark a cohesive effort toward creating more capable and versatile robots. These systems, when integrated, promise a future where robots seamlessly navigate complex environments, make faster decisions, and adapt skills to novel situations. While still in the research prototype stage, these innovations highlight DeepMind’s progress in overcoming challenges in robotics. Through them, Google is paving the way for robots to integrate seamlessly into our daily lives.

Also Read: DeepMind RoboCat: A Self-Learning Robotic AI Model

Our Say

As we witness the unveiling of Google DeepMind’s latest robotics advancements, it’s clear that we are on the brink of a transformative era in robotics. The integration of large-scale data collection, efficiency improvements, and motion generalization opens doors to a myriad of possibilities for intelligent helper robots. These innovations not only enhance current robotic capabilities but also lay the groundwork for future breakthroughs in the field. The future envisioned by Google DeepMind is one where AI-powered robots become indispensable companions. A future where robots are capable of understanding and executing complex tasks with precision and adaptability.

K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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