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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

Will this Google Deepmind Robot Play Table Tennis in the 2028 Olympics?

Pankaj Singh
Pankaj Singh Last Updated : 18 Aug, 2024
6 min read

Introduction

We have said au revoir to the Olympic Games Paris 2024, and the next will be held after 4 years, but the development by Google DeepMind may signal a new era in sports and robotics development. I recently came across a fascinating research paper (Achieving Human-Level Competitive Robot Table Tennis) by Google DeepMind that explores the capabilities of robots in table tennis. The study highlights how the advanced robot can play against human opponents of various skill levels and styles; the Robot features 6 DoF ABB 1100 arms mounted on linear gantries and achieves an impressive win rate of 45%. It’s incredible to think about how far robotics has come!

It’s only a matter of time before we witness a Robot Olympics, where nations compete using their most advanced robotic athletes. Imagine robots racing in track and field events or battling it out in competitive sports, showcasing the pinnacle of artificial intelligence in athletics.

Picture this: you are witnessing a robot, with the precision and agility of an experienced player, skillfully playing table tennis against a human opponent. What would your reaction be? This article will discuss a groundbreaking achievement in robotics: creating a robot that can compete at an amateur human level in table tennis. This is a significant leap towards achieving human-like robotic performance.

Google Deepmind Robot Table Tennis

Overview

  1. Google DeepMind’s table tennis robot can play at an amateur human level, marking a significant step in real-world robotics applications.
  2. The robot uses a hierarchical system to adapt and compete in real time, showcasing advanced decision-making abilities in sports.
  3. Despite its impressive 45% win rate against human players, the robot struggled with advanced strategies, revealing limitations.
  4. The project bridges the sim-to-real gap, allowing the robot to apply learned simulation skills to real-world scenarios without further training.
  5. Human players found the robot fun and engaging to play against, emphasizing the importance of successful human-robot interaction.

Table of contents

The Ambition: From Simulation to Reality

Barney J. Reed, Professional Table Tennis Coach, said: 

Truly awesome to watch the robot play players of all levels and styles. Going in our aim was to have the robot be at an intermediate level. Amazingly it did just that, all the hard work paid off.

I feel the robot exceeded even my expectations. It was a true honor and pleasure to be a part of this research. I have learned so much and am very thankful for everyone I had the pleasure of working with on this.

The idea of a robot playing table tennis isn’t merely about winning a game; it’s a benchmark for evaluating how well robots can perform in real-world scenarios. Table tennis, with its rapid pace, needs for precise movements, and strategic depth, presents an ideal challenge for testing robotic capabilities. The ultimate goal is to bridge the gap between simulated environments, where robots are trained, and the unpredictable nature of the real world.

This project stands out by employing a novel hierarchical and modular policy architecture. It’s a system that isn’t just about reacting to immediate situations and understanding and adapting dynamically. Low-level controllers (LLCs) handle specific skills—like a forehand topspin or a backhand return—while high-level controllers (HLC) orchestrate these skills based on real-time feedback.

The complexity of this approach cannot be overstated. It’s one thing to program a robot to hit a ball; it’s another to have it understand the context of a game, anticipate an opponent’s moves, and adapt its strategy accordingly. The HLC’s ability to choose the most effective skill based on the opponent’s capabilities is where this system really shines, demonstrating a level of adaptability that brings robots closer to human-like decision-making.

High and Low Level Controller

Also read: Beginners Guide to Robotics With Python

Breaking Down the Zero-Shot Sim-to-Real Challenge

One of the most daunting challenges in robotics is the sim-to-real gap—the difference between training in a controlled, simulated environment and performing in the chaotic real world. The researchers behind this project tackled this issue head-on with innovative techniques that allow the robot to apply its skills in real-world matches without needing further training. This “zero-shot” transfer is particularly impressive and is achieved by an iterative process where the robot continuously learns from its real-world interactions.

What’s noteworthy here is the blend of reinforcement learning (RL) in simulation with real-world data collection. This hybrid approach allows the robot to progressively refine its skills, leading to an ever-improving performance grounded in practical experience. It’s a significant departure from more traditional robotics, where extensive real-world training is often required to achieve even basic competence.

Also read: Robotics and Automation from a Machine Learning Perspective

Performance: How Well Did the Robot Actually Do?

Robot Table Tennis

In terms of performance, the robot’s capabilities were tested against 29 human players of varying skill levels. The results? A respectable 45% match win rate overall, with particularly strong showings against beginner and intermediate players. The robot won 100% of its matches against beginners and 55% against intermediate players. However, it struggled against advanced and expert players, failing to win any matches.

These results are telling. They suggest that while the robot has achieved a solid amateur-level performance, there’s still a significant gap in competing with highly skilled human players. The robot’s inability to handle advanced strategies, particularly those involving complex spins like underspin, highlights the system’s current limitations.

Also read: Reinforcement Learning Guide: From Fundamentals to Implementation

User Experience: Beyond Just Winning

Google Deepmind Robot

Interestingly, the robot’s performance wasn’t just about winning or losing. The human players involved in the study reported that playing against the robot was fun and engaging, regardless of the match outcome. This points to an important aspect of robotics that often gets overlooked: the human-robot interaction.

The positive feedback from users suggests that the robot’s design is on the right track in terms of technical performance and creating a pleasant and challenging experience for humans. Even advanced players, who could exploit certain weaknesses in the robot’s strategy, expressed enjoyment and saw potential in the robot as a practice partner.

This human-centric approach is crucial. After all, the ultimate goal of robotics isn’t just to create machines that can outperform humans but to build systems that can work alongside us, enhance our experiences, and integrate seamlessly into our daily lives.

You can watch the full-length videos here: Click Here.

Also, you can read the full research paper here: Achieving Human-Level Competitive Robot Table Tennis.

Critical Analysis: Strengths, Weaknesses, and the Road Ahead

Robot Table Tennis

While the achievements of this project are undeniably impressive, it’s important to analyze the strengths and the shortcomings critically. The hierarchical control system and zero-shot sim-to-real techniques represent significant advances in the field, providing a strong foundation for future developments. The ability of the robot to adapt in real-time to unseen opponents is particularly noteworthy, as it brings a level of unpredictability and flexibility crucial for real-world applications.

However, the robot’s struggle with advanced players indicates the current system’s limitations. The issue with handling underspin is a clear example of where more work is needed. This weakness isn’t just a minor flaw—it’s a fundamental challenge highlighting the complexities of simulating human-like skills in robots. Addressing this will require further innovation, possibly in spin detection, real-time decision-making, and more advanced learning algorithms.

Also read: Top 6 Humanoid Robots in 2024

Conclusion

This project represents a significant milestone in robotics, showcasing how far we’ve come in developing systems that can operate in complex, real-world environments. The robot’s ability to play table tennis at an amateur human level is a major achievement, but it also serves as a reminder of the challenges that still lie ahead.

As the research community continues to push the boundaries of what robots can do, projects like this will serve as critical benchmarks. They highlight both the potential and the limitations of current technologies, offering valuable insights into the path forward. The future of robotics is bright, but it’s clear that there’s still much to learn, discover, and perfect as we strive to build machines that can truly match—and perhaps one day surpass—human abilities.

Let me know what you think about Robotics in 2024…

Frequently Asked Questions

Q1. What is the Google DeepMind table tennis robot?

Ans. It’s a robot developed by Google DeepMind that can play table tennis at an amateur human level, showcasing advanced robotics in real-world scenarios.

Q2. How does the robot adapt during a game?

Ans. It uses a hierarchical system, with high-level controllers deciding strategy and low-level controllers executing specific skills, such as different types of shots.

Q3. What challenges did the robot face in table tennis matches?

Ans. The robot struggled against advanced players, particularly with handling complex strategies like underspin.

Q4. What is the ‘zero-shot sim-to-real’ challenge?

Ans. It’s the challenge of applying skills learned in simulation to real-world games. The robot overcame this by combining simulation with real-world data.

Q5. How did players feel about playing against the robot?

Ans. Regardless of the match outcome, players found the robot fun and engaging, highlighting successful human-robot interaction.

AI robotArtificial IntelligenceGoogle DeepMindGoogle Deepmind RobotGoogle Deepmind Robot Table Tennismachine learningRobotics
Pankaj Singh
Pankaj Singh

Hi, I am Pankaj Singh Negi - Senior Content Editor | Passionate about storytelling and crafting compelling narratives that transform ideas into impactful content. I love reading about technology revolutionizing our lifestyle.

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