Embedding Intelligence in Everyday Objects with TJBot
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TJBot is a DIY open source kit that allows you to build your own programmable cardboard robot powered by Watson. It consists of a cardboard cutout (which can be 3D printed or laser cut), Raspberry Pi and a variety of add-ons – including a RGB LED light, a microphone, a servo motor, and a camera. This presentation provides an overview of how Watson Cognitive Services are leveraged to create capabilities within TJBot, and how to build simple applications for TJBot using Node.js.
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Embedding Intelligence in Everyday Objects with TJBot
- 1. IBM Research 1 IBM Research Victor Dibia Embedding Intelligence in Everyday Objects with TJBot. An open source DIY project powered by Watson Cognitive Services. Human – Agent Collaboration Lab, IBM Research dibiavc@us.ibm.com @vykthur | github.com/victordibia Feb 20, 2017
- 2. IBM Research 2 TJBot : What and Why? - Open source DIY project to get you engaged with Watson Services
- 3. IBM Research 3 What is TJBot? - A cardboard robot - Simple, approachable - Open Source (design, code) - Cognitive (IBM Watson services) - Extensible (prototyping platform) Components: Raspberry Pi, LED, Camera, Microphone, Speaker, Servo.
- 4. IBM Research 4 3D Print Laser cut
- 6. IBM Research 6 Recipes. Step by step instructions + Code (node.js) to help you prototype capabilities for TJBot powered by Watson services. http://www.instructables.com/member/TJBot/
- 7. IBM Research 7 Project Goals How can we make it easier to engage a community of enthusiasts experimenting with embodied cognition – the idea of embedding intelligence in everyday objects within the physical world?
- 8. IBM Research 8 Project Goals Design principle – Approachable Design - Use of familiar material (cardboard) that can be altered with ease. - Simplified part assembly: no soldering or adhesive required. - Simplified programming model and language interface (JavaScript).
- 9. IBM Research 9 Project Outcome A prototyping platform to help democratize Embodied Cognition. Target communities: - Makers - Developers - Students (Education and Learning)
- 10. IBM Research 10 How Does Watson Enable TJBot? Listen Watson Speech to Text service converts spoken speech to text that can be analyzed Speak Watson Text to Speech service service converts text to sound using various voices. Understand Emotions Watson Tone Analyzer service can infer the emotion within text. E.g.. it can tell if a message contains emotions like happy , sad, angry Understand Conversations Watson Conversation Service can respond to users in a way that simulates a conversation between humans. See Watson Visual Recognition service can understand the content of an image and describe it.
- 11. IBM Research 11TJBot Sensors Example Capabilities Example Watson Services Example Use cases LED Speakers CameraServo Motor Arm Microphone Listen Speak Shine Show emotion Wave See Speech to text Tone Analyzer Vision Recognition Conversation Text to speech Sentiment Analysis Virtual Agents (eldercare, home care) Education (language learning)
- 12. IBM Research 12 Demo. - Watson Services - Speech to text - Text to speech - Conversation - Visual Recognition
- 13. IBM Research 13 Overview of Watson Services
- 14. IBM Research 14 IBM Watson Cognitive Take your first step into the cognitive era with our variety of smart services. Services. - Natural interaction - Semi-structured data processing - Trained and continuously improved via machine learning and deep learning. - Restful API services with SDKs for node.js, java, python.
- 18. IBM Research 18 Speech to Text Converts audio voice into written text. • Transcription • Voice-controlled applications: allows for custom models https://speech-to-text-demo.mybluemix.net/
- 19. IBM Research 19 Text To Speech Converts written text into natural sounding audio in a variety of languages and voices. • Customize and control the pronunciation of specific words to deliver a seamless voice interaction that catered s to your audience. • Interactive voice based applications. https://text-to-speech-demo.mybluemix.net/
- 20. IBM Research 20 Tone Analyzer Uses linguistic analysis to detect three types of tones in written text: emotions, social tendencies, and writing style. • Understand emotional context in conversations or communications • Taylor interaction based on sentiment. https://tone-analyzer-demo.mybluemix.net/
- 21. IBM Research 21 Visual Recognition Understands the contents of images - visual concepts tag the image, find human faces, approximate age and gender, and find similar images in a collection. • Train the service by creating your own custom concepts. Use Visual Recognition to detect a dress type in retail, identify spoiled fruit in inventory, and more. https://visual-recognition-demo.mybluemix.net/
- 22. IBM Research 22 AlchemyLanguage Analyzes text to help you understand its concepts, entities, keywords, sentiment, and more. • Additionally, you can create a custom model for some APIs to get specific results that are tailored to your domain. https://alchemy-language-demo.mybluemix.net/
- 23. IBM Research 23 Conversation Quickly build, test and deploy a bot or virtual agent across mobile devices, messaging platforms like Slack or even on a physical robot. • Visual dialog builder to help you create natural conversations between your apps and users, without any coding experience required. https://conversation-demo.mybluemix.net/
- 24. IBM Research 24 Programming TJBot - Getting started - Tying stuff together
- 25. IBM Research 25 Steps. - Wifi Setup, - Raspberry Pi Software update - Hardware setup : LED, Servo, Microphone, etc - Credential Setup : Bluemix - Recipe software setup : Clone Github repo - Ready to go.
- 27. IBM Research 27 Libraries Used Depends on several npm packages. - RGB LED – ws281x library - Servo – pigpio software PWM library - Microphone – mic library - Speaker – aplay library - Camera – raspistill wrapper
- 28. IBM Research 28 Code Walk through: Control LED on TJBot using voice. - http://www.instructables.com/i d/Use-Your-Voice-to-Control-a- Light-With-Watson/ - Code Walk through
- 29. IBM Research 29 TJBot Library [Beta] - Experimental work to encapsulate basic functions of the bot. - https://github.com/ibmtjbot/tjbotlib
- 30. IBM Research 30 The TJBot Library Encapsulate basic functions for TJBot such as listening, speaking, led color change, waving, seeing.
- 31. IBM Research 31 The TJBot Library tj.listen(transcript callback) tj.speak(“text”) tj.converse() tj.see() tj.shine(“red”)
- 32. IBM Research 32 Code Walk through: Control LED using the TJBot library. - https://github.com/ibmt jbot/recipes - Code Walk through
- 33. IBM Research 33 Open Issues - Improving accuracy - Bot “Interruptibility” - Gracefully managing latency
- 34. IBM Research 34 Improving Accuracy How do we improve interaction (voice) accuracy? Improving Speech-to-Text models may not be enough! - Customized language models? - Intent Matching? - Multi-turn conversations?
- 35. IBM Research 35 Bot “Interruptibility” When and how should the robot be interrupted (while performing an activity like speaking, waving etc.)? - Vision? (monitoring a user’s facial expression, raised hand) - Hardware button or sensor?
- 36. IBM Research 36 Latency Tolerance Latency can severely degrade quality of interaction. How do we minimize its effect? - Managing and ordering service responses - Leverage cues to provide additional information - Balancing capabilities – cloud vs local processing.
- 38. IBM Research 38 Next Steps 3 pronged - Conduct basic research that address open issues. - Make TJBot simpler and easier to use (tjbot library, visual programming tool) - Build and sustain the TJBot community.
- 39. IBM Research 39 Learn more? - Ibm.biz/mytjbot - http://www.instructables.com/ member/TJBot/
Notas do Editor
- Presentation Overview Hi all, thanks for coming to this session, and thanks to the organisers for letting us share this project with this awesome audience. I am new to this meetup group, and I look forward to attending more events in the near future! By show of hands, just to know the audience – how many of us I am a researcher with the Human Agent collaboration group over at IBM research, Yorktown and we are a group of individuals this background in software engineering, HCI,behavioural psychology, cognitive sciences, robotics and mathematics. Pretty diverse group. Some of the projects we have worked on include cognitive M&A, designing interfaces for wearables and wearable apps, learning and navigation systems for robots etc. My background has elements of software engineering, HCI and behavioural psychology. ===== Today, I am going to talk about a relatively new project some members of our group have worked on TJBot …. This effort is co-led by my colleague Maryam Ashoori and our current work focus is related to designing novel interfaces for cognitive application development. What are the issues that arise ? What are viable use cases ? What are applicable design patterns I’ll start with an overview of the TJBot project, why we are working on it. I’ll also go over Watson Services which is a key aspect of this project . Finally, I ‘ll go over technilcal details for programming TJBot and discuss some open issues we hope to learn more about as the project proceeds. Take away … Additional infromation on Watson services How Watson can be embedded in objects to enable capabilities How you can start prototyping embodied cognition apps with TJBot Im not a developer advocate … so there might be service speciifc questions I cannot answer at the moment, but if you leave your context or tweet at me, I’ll ensure I follow up.
- After we came up with the initial design,
- Simplicity Hardware LED and hardware connection Snap to build approach, no tools, glue or adhessive required. Software Javascript, the most widely used programming language Nodejs and watson developer
- Simplicity Hardware LED and hardware connection Snap to build approach, no tools, glue or adhessive required. Software Javascript, the most widely used programming language Nodejs and watson developer
- At our research group we care about questions related to understanding the various ways in which we as humans can can work with machine in a symbiotic computing environment. We explore how, intelligence can be embedded into spaces (rooms), objects, avatars and icons etc. A subdomain on these area has to do with the design of cognitive objects and embodied cognition. It is nice to develop web based or mobile phone based cognitive apps (e.g. chatbots) Embodied Cognition Embedding intelligence in every day objects. Understanding how physical attributes integrate with intelligent services to craft engaging user experiences. Simplicity Hardware LED and hardware connection Snap to build approach, no tools, glue or adhessive required. Software Javascript, the most widely used programming language Nodejs and watson developer Embodied cognition is a research program comprising an array of methods from diverse theoretical fields (e.g., philosophy, neuroscience, psychology, etc.) held together by the key assumption that the body functions as a constituent of the mind rather than a passive perceiver and actor serving the mind.
- Helping bridge gaps for two groups of people .. Makers and developers and also working as a tool for curriculum development around cognitive application development
- Embodied Cognition When we simplify the process sufficiently, what would people create and
- The library is meant to encapsulate basic capabilities of the robot Avoid code repetition Streamline programming paradigm and patterns Free developers from the nitty gritty of hardware controls so they can focus on being creative about usecases
- Embodied Cognition Natural interaction with humans For example speech and emotions Not only used by web applications, but also in physical world Processing of semi-structured data and big amounts of data For example language classifications and image recognitions Trained and continuously improved via machine and deep learning For example search (retrieve and rank) Leverages context to improve service qualities
- Embodied Cognition
- Embodied Cognition
- TranscriptionUse speech to text to create voice-controlled applications – even customize the model to improve accuracy for the language and content you care about most such as product names, sensitive subjects, or names of individuals.
- converts written text into natural sounding audio in a variety of languages and voices. You can customize and control the pronunciation of specific words to deliver a seamless voice interaction that catered s to your audience. Use text to speech to develop interactive toys for children, automate call center interactions, and communicate directions hands-free. https://text-to-speech-demo.mybluemix.net/
- Tone Analyzer uses linguistic analysis to detect three types of tones in written text: emotions, social tendencies, and writing style. Use the Tone Analyzer service to understand emotional context of conversations and communications. Use this insight to respond in an appropriate manner. https://tone-analyzer-demo.mybluemix.net/
- Visual Recognition understands the contents of images - visual concepts tag the image, find human faces, approximate age and gender, and find similar images in a collection. You can also train the service by creating your own custom concepts. Use Visual Recognition to detect a dress type in retail, identify spoiled fruit in inventory, and more.
- analyze text to help you understand its concepts, entities, keywords, sentiment, and more. Additionally, you can create a custom model for some APIs to get specific results that are tailored to your domain. https://alchemy-language-demo.mybluemix.net/
- The library is meant to encapsulate basic capabilities of the robot Avoid code repetition Streamline programming paradigm and patterns Free developers from the nitty gritty of hardware controls so they can focus on being creative about usecases
- raspberry pi node.js libra
- Simplicity Hardware LED and hardware connection Snap to build approach, no tools, glue or adhessive required. Software Javascript, the most widely used programming language Nodejs and watson developer
- raspberry pi node.js libra
- The library is meant to encapsulate basic capabilities of the robot Avoid code repetition Streamline programming paradigm and patterns Free developers from the nitty gritty of hardware controls so they can focus on being creative about usecases
- The library is meant to encapsulate basic capabilities of the robot Avoid code repetition Streamline programming paradigm and patterns Free developers from the nitty gritty of hardware controls so they can focus on being creative about usecases
- The library is meant to encapsulate basic capabilities of the robot Avoid code repetition Streamline programming paradigm and patterns Free developers from the nitty gritty of hardware controls so they can focus on being creative about usecases
- The library is meant to encapsulate basic capabilities of the robot Avoid code repetition Streamline programming paradigm and patterns Free developers from the nitty gritty of hardware controls so they can focus on being creative about usecases
- Improving accuracy Improving accuracy of stt services is an ongoning effort, but this environment can be really noise so accurate speech models may not be enough. Gaol: Are there good ways to break down parts of an interaction such that we keep it natural but minimize errors TJBot play smooth criminal by michael jackson from spotify Play me some music Sure, what song would you like? Solution: Intent based matching Multi-turn conversation as a way to improve voice based accuracy Intterrupt: Goal: When and how should we interrupt the robot Should we build in approaches closesly tied with humans - e.g an uncormfortable expression, a raised hand, frowning face etc ? Solutions? Hardware interrupt ? Vision based interrupt ? (raised hand, frowning face) Latency. Can be a real problem, especially for a robot thats humanoid. He’s cute, but that will only take him so far. A primary source of latency has to do with making calls to cognitiev services, and the goal is to arrive Solution: Tools to manage response arrival, Leverage gestures (light, arm) to cue users Balancing computation – how much can we put on the Pi itself without degrading performance?
- Improving accuracy Improving accuracy of stt services is an ongoning effort, but this environment can be really noise so accurate speech models may not be enough. Gaol: Are there good ways to break down parts of an interaction such that we keep it natural but minimize errors TJBot play smooth criminal by michael jackson from spotify Play me some music Sure, what song would you like? Solution: Intent based matching Multi-turn conversation as a way to improve voice based accuracy Intterrupt: Goal: When and how should we interrupt the robot Should we build in approaches closesly tied with humans - e.g an uncormfortable expression, a raised hand, frowning face etc ? Solutions? Hardware interrupt ? Vision based interrupt ? (raised hand, frowning face) Latency. Can be a real problem, especially for a robot thats humanoid. He’s cute, but that will only take him so far. A primary source of latency has to do with making calls to cognitiev services, and the goal is to arrive Solution: Tools to manage response arrival, Leverage gestures (light, arm) to cue users Balancing computation – how much can we put on the Pi itself without degrading performance?
- Improving accuracy Improving accuracy of stt services is an ongoning effort, but this environment can be really noise so accurate speech models may not be enough. Gaol: Are there good ways to break down parts of an interaction such that we keep it natural but minimize errors TJBot play smooth criminal by michael jackson from spotify Play me some music Sure, what song would you like? Solution: Intent based matching Multi-turn conversation as a way to improve voice based accuracy Intterrupt: Goal: When and how should we interrupt the robot Should we build in approaches closesly tied with humans - e.g an uncormfortable expression, a raised hand, frowning face etc ? Solutions? Hardware interrupt ? Vision based interrupt ? (raised hand, frowning face) Latency. Can be a real problem, especially for a robot thats humanoid. He’s cute, but that will only take him so far. A primary source of latency has to do with making calls to cognitiev services, and the goal is to arrive Solution: Tools to manage response arrival, Leverage gestures (light, arm) to cue users Balancing computation – how much can we put on the Pi itself without degrading performance?
- The library is meant to encapsulate basic capabilities of the robot Avoid code repetition Streamline programming paradigm and patterns Free developers from the nitty gritty of hardware controls so they can focus on being creative about usecases
- Initial feedback has been postive .. (survey and twitter data) Individuals appear to want one before they even know what it does. Engagement .. Check! Wide availability – we have seen people download our 3d print and lasercut designs in places like south africa, pakistan, hong kong, chile, brazil, italy We want to take this further … make TJBot available to more people Solve some of the open problems and encapsulate what we learn in the TJBot library. Build and sustain the TJBot community.