Why Robotics has yet to have its ChatGPT moment
Robotics and ChatGPT: a match made in a sci-fi heaven, right?
Yet, despite our collective dreams of robot butlers, Asimov's three laws and fully self-driving everything, we’re still a bit short of that breakthrough moment.
Before we can have our robotics “ChatGPT moment”, we need something like a “PyTorch moment”. Imagine a user-friendly framework that allows researchers and developers to rapidly prototype, test, and iterate robotic systems with unprecedented ease and efficiency. This platform would:
The industrial and research scene's are packed with anticipation for Foundation Models for robots, but there is a shortage of people who can juggle deep ML and robotics.
The "PyTorch moment" for robotics is still an ongoing series of moments rather than a single event. However, strides have been made recently with the integration of frameworks like NVIDIA Isaac Sim and PyTorch. This combination has been accelerating AI in robotics, enabling advancements in tasks such as object detection, human pose estimation, object manipulation and image perception segmentation. As for autonomous vehicles, Elon Musk has boldly posted that “FSD will soon exceed 10,000 miles between critical interventions, which is a year of driving for most people.” Musk is renowned for being unrealistically optimistic about the timetable for solving unsupervised full self-driving and he's been quoted about the delivery of fully self-driving cars since 2016. But this time there’s mounting confidence that it’s merely a matter of time. So what needs to happen for "the Robotics moment" to arrive?
Deep Learning to the Rescue...
From perception to control, most AI issues in robotics require deep learning. Why did the neuron bring a suitcase to deep learning class? Because it was ready for some serious layers! Deep learning stacks layers upon layers of artificial neurons to process data and uncover patterns, just like packing for a long trip. Every layer digs deeper into understanding and interpreting the data, and before you know it, you’ve got some pretty smart luggage. In search of Tesla's self-driving car promises, the clearest indicator of progress has come with the removal of heuristic commands in C++ programming code entirely in favor of an end-to-end neural network that takes in visual data, processes it in real time though its AI inference chip onboard, and pilots the car. This AI approach combined with $10 billion on better AI training and advanced automated vehicle parking software has maneuvered some analysts to believe again in the possibility of a Tesla robotaxi capability in the near future.
Robot intelligence is basically a multi-headed hydra of AI problems: perception, mapping, planning etc etc... Deep learning is a would-be knight in shining armor across the entire stack—if only it could slay all those dragons simultaneously. Real-time learning, reasoning, memory, conceptual and contextual understanding are essential if Robotics and AI agents are ever going to serve millions of users at scale.
Antique Robotic Tools
Our older robotic tools are about as useful as a chocolate teapot for deep ML needs. Crafted in the days before cloud computing was favored, these relics struggle with parallel processing on GPU clusters. What we need is a total design revamp: data-first, parallel-friendly, and cloud-deep infrastructure. Matched by innovations in advanced materials, batteries, and AI for durable robot structures, enhancing energy efficiency, environmental awareness, responsiveness and adaptability.
Mosaics and Agentic Architectures
Programming robots efficiently is like trying to herd cats. Monolithic models? Meh. But mosaics and agentic architectures? Now we’re talking value. Mosaics in robotics involve creating robots with interchangeable, specialized components. This modular design makes robots adaptable and easier to maintain. Agentic architectures are designed to mimic cognitive abilities, enabling robots to to exhibit goal-directed behavior, learn from experience, and adapt to changing environments. Together, these concepts lead to advanced, adaptable, and intelligent robotic systems. Imagine programming robot behavior without breaking a sweat—well, maybe just a tiny one.
Cloud Connectivity Over Edge Compute
Connectivity is king, even for robots. Operator-based enterprises struggle with data management—ever tried cataloging a robot’s every move? Given that robotics is truly a multitasking domain – a robot needs to solve for multiple tasks at once. The cloud can handle multitasking at scale, from data management to simultaneous inference calls, potentially transforming the robotics game.
领英推荐
Safety First—Or Not?
Safety research in robotics is currently at a rather intriguing crossroads. Neurosymbolic representation and analysis might just be the golden key to applying safety frameworks in robotics. Neurosymbolic representation is like combining two types of smart: one that learns by finding patterns in data (neural networks) and one that reasons through problems using rules and logic (symbolic AI). It's like teaching a computer to both learn from experience and think through problems step by step—giving it a brain that's both intuitive and logical. If you want safe robots? This might be your ticket.
The Open Source Conundrum
Open source: the good, the bad, and the robot. While open source accelerates innovation, it brings its own set of headaches, especially for robotics. Fragmentation and silos slow things down, and large organizations might put the brakes on broad open-source initiatives. Maintaining an open source project requires significant time, effort, and resources to ensure high quality control and security against bad actors, which can be a burden for developers and organizations. It’s a balancing act, but one worth navigating.
In Conclusion: Advanced robots are inevitable
Robots are a part of our pop culture. From the ominous T-800 in Schwarzenegger's Terminator to the lovable WALL-E from Pixar, real-world robots are poised to become a staple in our lives, enhancing efficiency and productivity by executing tasks faster and with greater precision than humans. The rapid pace of AI, machine learning, and robotics innovation is propelling us toward a future of ever more sophisticated robots, expanding their capabilities and applications.
Able to work 24/7 without fatigue, they are transforming industries like manufacturing, logistics, and healthcare. In hazardous environments, robots take on risky tasks, shielding human workers from harm—think hazardous material handling, deep-sea exploration, or disaster response. With aging populations worldwide, robots help address labor shortages, especially in elderly care and domestic assistance.
Under economic pressures, businesses turn to robots to cut costs and boost efficiency by automating repetitive tasks. As we look to the stars, robots become essential for space exploration and potential colonization, handling harsh conditions and complex tasks that would challenge humans.
The first age of Intelligent Robots or "Robota" as Karel ?apek, a Czech novelist, coined for them will become the very definition of "forced labor" - a tough life, often with little room for social or economic mobility. In summary, robotics is teetering on the brink of its own “ChatGPT moment”. The journey's filled with challenges, but with the right focus, that breakthrough is more “when” than “if”.
Sources:
*Disclaimer: All the views and opinions expressed are those of the authors alone.