Tiago++ Takes Center Stage in euROBIN Competition
Service robots shine at euROBIN competition with Tiago++ showcasing impressive skills.
Fabio Amadio, Clemente Donoso, Dionis Totsila, Raphael Lorenzo, Quentin Rouxel, Olivier Rochel, Enrico Mingo Hoffman, Jean-Baptiste Mouret, Serena Ivaldi
― 8 min read
Table of Contents
- What is the euROBIN Coopetition?
- Meet Tiago++
- Voice Commands and Understanding Instructions
- Tasks and Challenges
- How Does Tiago++ Operate?
- Control and Teleoperation
- Object Pose Estimation
- Teaching and Learning
- Human Tracking
- Navigation
- Understanding Language
- Conclusion: The Future of Service Robots
- Original Source
- Reference Links
In the world of robotics, service robots are becoming increasingly common in homes and workplaces. One of the highlights of recent developments in this area is the participation of a robot named Tiago++ in a cooperative competition called euROBIN. This competition brought together various teams to show off their robots’ skills in a realistic kitchen setting, with a focus on voice-activated tasks.
This article explains the technology behind Tiago++, how it completed its tasks, and some of the challenges faced by teams during the competition. So, buckle up and prepare for an interesting exploration into the realm of service robots!
What is the euROBIN Coopetition?
EuROBIN stands for European Robotics in the Home. It's a competition that encourages collaboration among teams working on robotic technologies. Unlike traditional competitions, where the goal is to outdo each other, teams are rewarded for sharing their software and methods. This makes it a more friendly environment-imagine a group project where everyone helps each other instead of competing!
This year, the event took place in Nancy, France, with 20 teams participating in three different categories. The Service Robots League focused on mobile robots that can interact with people and objects in a home-like environment. Think of it as a little robotic Olympics but in your kitchen.
Meet Tiago++
Tiago++ is a modified version of a service robot that comes equipped with various advanced features. Its most impressive capabilities include a whole-body control system that allows it to operate autonomously or be controlled remotely by a human. This flexibility is crucial, as it allows the robot to handle unexpected situations or failures while performing tasks.
To show off its skills, Tiago++ needed to understand Voice Commands from humans and carry out specific tasks. The challenges included picking up an object from one place and moving it to another, or delivering an object to a person upon request. Easy-peasy, right? Well, not quite!
Voice Commands and Understanding Instructions
The ability to understand voice commands is essential for service robots like Tiago++. The robot uses a combination of speech recognition and a language model to interpret what is being said. This is a bit like having a human translator on standby to help the robot understand complicated instructions.
In competition, Tiago++ had to deal with a variety of human communication styles. Some people spoke clearly, while others might mumble or even use slang. The robot had to be programmed to handle all these variations. Think of it as trying to understand your Uncle Bob at the family barbecue after he’s had a few too many burgers-it’s not always easy!
Tasks and Challenges
During the competition, Tiago++ faced two main types of tasks:
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Pick and Place: This task required the robot to pick up objects from designated spots and place them somewhere else. It sounds simple, but doing this in a dynamic environment full of obstacles adds complexity to the task. Imagine trying to grab a snack from the kitchen cupboard while dodging your cat-frustrating, right?
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Delivery to a Person: For this task, the robot had to pick up an object and deliver it to someone. This was a bit like playing delivery driver but without the tips. The robot had to precisely navigate towards the person, ensuring that it was attentive enough to notice if they were paying attention. If the person was distracted, the robot could easily end up just standing there like that awkward friend at a party.
How Does Tiago++ Operate?
At its core, Tiago++ uses a mix of hardware and software to carry out tasks. The robot is equipped with an omnidirectional wheeled base, which means it can move in any direction. This is crucial for navigating tight spaces, like your kitchen when you’re trying to avoid that pile of dishes in the corner.
Tiago++ also has multiple cameras, which allow it to "see" what’s going on around it. These cameras help the robot detect objects and track people. It’s kind of like having a pair of very observant eyes, but instead of being judgmental, they’re just trying to do their job!
Control and Teleoperation
When the robot is not operating autonomously, a human can take control through a teleoperation interface. This means someone can manipulate the robot remotely, guiding it through situations that might be too challenging for it to handle alone. It's like playing a video game, only the character has a real-life body and can take a tumble if you're not careful!
The teleoperation interface uses inexpensive but effective components to communicate with the robot. This makes it easier for operators to take over when things go awry. The goal is to make the control as seamless as possible, so if Tiago++ runs into an unexpected issue, help is just a button press away.
Object Pose Estimation
For the robot to effectively act, it must know where objects are located. Tiago++ uses a method called pose estimation to figure this out. This basically means it can identify objects' positions in three-dimensional space. The robot uses special markers called AprilTags, which act like little signs that help it pinpoint where things are.
Using cameras mounted on the robot, Tiago++ gathers information about the environment. This way, it can navigate around without knocking over your grandmother's prized vase!
Teaching and Learning
One of the critical aspects of the competition was teaching Tiago++ how to perform specific tasks. The robot learned through demonstrations, where a human operated it to show how to complete a task. This method of learning is similar to how we learn by watching others, and it can be quite effective.
The Tiago++ system records these demonstrations and uses them to generate a plan for future tasks. However, this learning method may not be perfect. If the robot encounters a scenario that deviates from what it has learned, it might struggle a bit. Think of it as trying to bake a cake without needing the recipe for every variation-it might turn out okay, or it might be a complete disaster!
Human Tracking
Another essential capability of Tiago++ is its ability to track humans within its environment. This is important for tasks that require interaction. If the robot is to hand over an item to a person, it first needs to locate them and ensure they're paying attention.
The robot uses a combination of cameras and algorithms to detect and track individuals. It involves several steps, like identifying humans, estimating their position, and checking if they’re looking at the robot. If someone is staring at their phone, Tiago++ doesn't want to interrupt their scrolling session!
Navigation
Navigating through a cluttered environment is a tricky task for any robot. Tiago++ relies on a basic navigation system that helps it determine its location and move toward its goals. This system utilizes various sensors to detect obstacles and plan routes efficiently.
While its navigation may not be as advanced as some other systems, the simplicity worked well for the competition's context. No need to overthink things-sometimes, a straight path is all you need!
Understanding Language
To understand and respond to human instructions, Tiago++ employs language processing techniques. This involves converting spoken words into text and then interpreting that text to generate a plan. The robot uses a specific software to help bridge the gap between what people say and what the robot needs to do.
One of the challenges here is the variety of speaking styles. Just like in life, some people can be very clear, while others might speak in riddles. The goal is to ensure that whatever way a command is given, the robot can figure it out. It's like having a best friend who can understand your slang without needing a translation guide!
Conclusion: The Future of Service Robots
The euROBIN coopetition highlighted some exciting advancements in robotic technology. While complications still arise when deploying service robots in everyday situations, innovations like Tiago++ demonstrate that we are moving in the right direction. With each competition, the field of robotics becomes more refined, and the robots become more capable.
As technology continues to evolve, who knows what future competitions will look like? Perhaps, next time, we'll see service robots not only making deliveries but also serving dinner and doing our laundry-now, that's something to look forward to!
In summary, Tiago++ showed that with the right technology, teamwork, and a sprinkle of humor, service robots can be valuable helpers in our homes. With continued dedication and innovation, the future of robotics holds exciting possibilities for all of us-maybe even a robot that can fetch your remote control!
Title: From Vocal Instructions to Household Tasks: The Inria Tiago++ in the euROBIN Service Robots Coopetition
Abstract: This paper describes the Inria team's integrated robotics system used in the 1st euROBIN coopetition, during which service robots performed voice-activated household tasks in a kitchen setting.The team developed a modified Tiago++ platform that leverages a whole-body control stack for autonomous and teleoperated modes, and an LLM-based pipeline for instruction understanding and task planning. The key contributions (opens-sourced) are the integration of these components and the design of custom teleoperation devices, addressing practical challenges in the deployment of service robots.
Authors: Fabio Amadio, Clemente Donoso, Dionis Totsila, Raphael Lorenzo, Quentin Rouxel, Olivier Rochel, Enrico Mingo Hoffman, Jean-Baptiste Mouret, Serena Ivaldi
Last Update: Dec 20, 2024
Language: English
Source URL: https://arxiv.org/abs/2412.17861
Source PDF: https://arxiv.org/pdf/2412.17861
Licence: https://creativecommons.org/licenses/by/4.0/
Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.
Thank you to arxiv for use of its open access interoperability.
Reference Links
- https://www.eurobin-project.eu/index.php/competitions/coopetitions
- https://www.eurobin-project.eu/
- https://pal-robotics.com/robot/tiago
- https://youtu.be/5mSIYuH4Mdk
- https://github.com/ADVRHumanoids/OpenSoT
- https://github.com/ADVRHumanoids/CartesianInterface
- https://github.com/hucebot/tiago_dual_cartesio_config/tree/euRobin_nov24
- https://github.com/hucebot/dxl_6d_input/tree/bimanual-teleoperation
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- https://github.com/SYSTRAN/faster-whisper
- https://github.com/ggerganov/llama.cpp
- https://github.com/hucebot/fsm_cartesio
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