What Does A Foundation Model For Robotics Look Like
What is it, what should it be capable of, where will the training data come from, who's working on it
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2024 is gearing up to be the year of robotics. There are 4 different trends converging to make this happen:
Robotics hardware is getting standardized, which enables more developers to participate in the ecosystem.
Sensors are becoming stronger and less expensive, so you can instrument your robot to perceive the environment a lot better.
AI models are becoming incredibly capable when it comes to multimodal data.
There's enough compute power available at the edge to run these AI models.
I've been thinking about what a robotics foundation model would look like. Foundation model for text is called LLM. And it has taken the world by storm. But what about a foundation model for robotics? What should it be capable of and what can it unlock? The goal of this post is to answer those questions.
Can you explain the term "Robotics Foundation Model" in simple terms?
A Robotics Foundation Model is like a brain for robots. It is software+algorithms that enable a robot to:
Understand its environment: Perceive its surroundings, detect objects, and track movements.
Make decisions: Plan tasks, navigate, and interact with its environment.
Perform tasks: Execute actions, manipulate objects, and adapt to changes.
This foundation model acts as the robot's operating system that integrates various components such as sensors, actuators, and computing resources to enable the robot to perform tasks autonomously.
Let's go a level deeper. What are all the things a Robotics Foundation Model should be capable of?
I'm going to divide it into 6 categories:
1. Perception
It should be able to integrate various sensors such as cameras, lidars, GPS, IMU, and others to perceive the environment. It should be able to detect and recognize objects. This includes people, obstacles, and other robots. Another key point is to understand the layout of the environment. This includes spatial relationships between objects. It should be able to track objects over time including their movement and behavior.
2. Motion and Control
It should plan and execute motion paths to achieve tasks. It should avoid obstacles and ensure safety. It should control the robot's movements. This includes velocity, acceleration, and deceleration. It should help maintain stability and balance in uneven environments. And it should perform tasks that require manipulation e.g. grasping, lifting, placing objects.
3. Cognition and Reasoning
It should plan and execute tasks. This includes sequencing, scheduling, and resource allocation. It should be able to make decisions based on sensor data, goals, and constraints. It should learn from experience, adapt to new situations, and improve performance over time. It should be able to reason about the environment, objects, and tasks to make informed decisions. And it should be able to operate at various autonomy levels. This includes teleoperation, supervised autonomy, and full autonomy.
4. Communication
It should be able to interact with humans through natural language, gestures, or other modalities. It should be able to communicate and coordinate with other robots to achieve common goals. It should delegate tasks to other robots or agents. And coordinate their execution.
5. Performance Monitoring
It should be able to monitor the robot's state and environment to ensure safe operation. It should detect and recover from faults. This includes hardware and software failures. It should Prevent unauthorized access or malicious behavior. And comply with relevant safety and security standards, regulations, and laws.
6. Integration
It should integrate with various software frameworks e.g. ROS (Robot Operating System). It should abstract hardware components such as sensors, actuators, and computing resources. It should integrate with middleware solutions such as message queues, data storage, and more. And it should integrate with computing services including data processing and AI infrastructure.
Where will the training data come from to build this Robotics Foundation Model?
Training a Robotics Foundation Model requires a massive amount of data. It needs to be diverse, high-quality, and well-annotated. You need to cover a lot of edge cases as well. Here are the potential sources of training data:
Real-World Robot Data: You can collect data from robots operating in various environments, such as warehouses, homes, or outdoor spaces. This data can be collected through sensors, cameras, and other devices mounted on the robot. You can also crowdsource it. You can collect the data from a large number of robots or devices, similar to how crowdsourced data is collected for self-driving cars. This can provide a massive amount of diverse data.
Simulation Environments: Simulation platforms like Gazebo, V-REP, or Webots can generate large amounts of synthetic data. This includes sensor readings, images, and other relevant information. This data can be used to train the model in a virtual environment before deploying it on a physical robot.
Synthetic Data Generation: You can use generative models to generate synthetic data that mimics real-world scenarios. This can help augment the training data and improve the model's robustness.
Human-Robot Interaction: You can collect data from human-robot interaction e.g. voice commands, gestures. This data can help the model learn to understand and respond to human input.
Open-Source Datasets: You can leverage open-source datasets e.g. ROS datasets. It provides a wide range of robotics-related data including sensor readings, images, and more.
Data Annotation Services: You can use data annotation services that can provide human-annotated data for robotics-specific tasks e.g. object detection, segmentation, tracking.
Who's working on building Robotics Foundation Models?
There are many people across different sectors working on different flavors of this. You can check out the work of the following list of companies/organizations to learn more about what they're working on:
Robotics companies: Boston Dynamics, Nuro, Starship Technologies, Agility Robotics, Osaro, Covariant, Dexterity
Universities: MIT CSAIL, Stanford Robotics Lab, CMU's Robotics Institute, UC Berkeley's Robotics and AI Lab
Bigtech: Google, Amazon, Microsoft
Autonomous vehicle companies: Waymo, Cruise, Argo AI, Tesla
Robotics platforms: ROS, NVIDIA's Isaac SDK, AWS RoboMaker
Industrial companies: Siemens, ABB, FANUC
If you're a founder or an investor who has been thinking about this, I'd love to hear from you. I’m at prateek at moxxie dot vc.
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