It feels like every week brings another big announcement in AI and robotics - whether it’s another startup, another breakthrough, or another bold claim. Some of these are legitimate advances, while others are polished demos wrapped in marketing hype. But beneath it all, there are real trends shaping the future of automation. We see three big ones:
- Bigger Models, Bigger Promises - Since deep learning took off, two ideas have dominated: bigger networks have more potential, and better data leads to better learning. Many believe today’s architectures are already enough to reach human-level intelligence, we just need to scale them up and fine-tune the algorithms.
- The Transformer Effect - Transformers have redefined AI. Originally designed to improve memory and context in deep networks, they’ve unlocked massive gains in generative AI. The ability to process sequences of text, video, and more has made AI much more dynamic and responsive. While these ideas have existed before, transformers have supercharged their potential.
- The Robotics Renaissance - The excitement around AI is fueling renewed interest in physical automation. Humanoid robots, in particular, are drawing massive investment. But here we see a problem: building a human-shaped machine that can move, sense, and reason is an incredibly hard challenge, and not always the best solution. Instead of trying to replace people, we believe in designing AI-powered automation that works with them.
The Role of Plus One Robotics
Where does Plus One fit in? We specialize in warehouse automation where reliable AI needs to function in real-world environments with limited internet connectivity. Unlike self-driving cars, our systems don’t have to operate in real-time at high speeds, and we have a built-in advantage - our Crew Chiefs. They provide human oversight to ensure efficiency, adapting the system to new challenges as they arise.
Looking Ahead: Smarter, More Adaptable Robotics
The longer-term vision for robotics is plug-and-play automation with systems that just work. While some are betting on humanoids as the ultimate solution, we believe the smarter approach is building adaptable, intuitive robots that integrate seamlessly into existing workflows.
In the next five years, our robots will still be mechanical arms powered by advanced vision and AI. But the real transformation will be in their intelligence. Here’s where we’re focusing:
- Better Metrics—We track system performance through operations efficiency and throughput metrics. However, additional metrics, such as entitlement, are difficult to measure or track. Systems that directly track and optimize these more elusive metrics could offer greater utility.
- Learning on the Job - Right now, human engineers tweak system parameters. The goal is for the robots to refine themselves over time.
- Expanding Control - Our AI directs robotic arms, but broader automation includes conveyors, PLCs and other systems. Integrating control across these components could unlock greater efficiency.
- Smarter Data Sharing - Today, we manually aggregate data across multiple sites. The next step is making this process intelligent, finding the right balance between generalization and specialization.
- Better Human Interfaces - Our Crew Chiefs play a key role in teaching the system, but current tools limit their ability to provide detailed input. We need to refine these interactions to make learning faster and more precise.
AI-Powered Robotic Grasping: The Next Leap
We’ve used AI for years for object detection, but “grasping,” the actual motion of picking up an item, has relied on algorithms coded by hand. These rules are complex, time-consuming to fine-tune, and challenging to scale.
Advances in transformers and multi-modal AI allow us to consider end-to-end learning, replacing traditional heuristics with a model that predicts optimal grasping directly from sensor data. Instead of handcrafting every step, we can train a system to generalize across scenarios.
Various organizations are investigating this problem, with some producing impressive demonstrations. However, none have developed an end-to-end learning system surpassing our hand-crafted solution. This isn't a fundamental limitation of learning capabilities but rather of current learning methodologies.
By integrating these end-to-end models with our existing system, using the former to enhance the latter, and continuously learning through human-in-the-loop collaboration, we can create a comprehensive system that leverages the strengths of all its component technologies.
The Road Ahead
Robotics is evolving fast. The key to success isn’t chasing the biggest model or the flashiest demo, it’s finding practical, scalable ways to apply AI in the real world today. At Plus One Robotics, we’re not just following trends. We’re building systems that work while laying the foundation for improved automation tomorrow.