DeepSummary
Peter Chen, the co-founder and CEO of Covariant, discusses his journey from research in reinforcement learning and unsupervised learning at UC Berkeley and OpenAI to starting Covariant, a robotics company focused on developing AI robots for warehouses and logistics. He explains Covariant's approach of building foundation models that can learn from large amounts of data and adapt to diverse environments, enabling robots to handle complex tasks like picking and packing items in warehouses.
One of Covariant's key insights was the need to collect vast amounts of real-world data from robots in production environments to train their foundation models effectively. They decided to start a company to serve customers and collect this data, following a similar approach to Tesla's self-driving cars. Chen emphasizes the importance of precise grounding and understanding of the physical world for robotics AI, which requires high-quality data beyond what's available on the internet.
Chen discusses the current limitations of manufacturing robotics, the challenges of grasping and identifying objects, and Covariant's plans for expanding to new tasks and hardware form factors like humanoid robots. He also shares his thoughts on the future of robotics, including the potential for a 'Chat GPT moment' where robots can adapt to arbitrary new scenarios with high reliability, and the role of robotics in advancing general AI capabilities.
Key Episodes Takeaways
- Covariant is building adaptive AI robots for warehouses and logistics by training foundation models on large amounts of real-world data collected from robots in production environments.
- Precise grounding and understanding of the physical world is crucial for robotics AI, which requires high-quality data beyond what's available on the internet.
- Covariant believes the key to advancing robotics AI is collecting the most real-world robotics data, which they achieve by deploying robots with customers to gather data while providing value.
- Achieving a 'Chat GPT moment' for robotics requires AI systems that are not only general and adaptable, but also highly reliable in their operations.
- Safety is a key consideration for Covariant, and they adhere to existing safety regulations for industrial robots to ensure their AI systems operate safely.
- Covariant plans to expand their AI robots to new tasks within warehousing and logistics, and eventually to new hardware form factors like humanoid robots.
- Robotics is seen as a key driver for advancing general AI capabilities, as it provides grounded, embodied data from physical interactions with the world.
- Covariant's approach follows an incremental roadmap, building products that can deliver value while collecting data to train increasingly capable AI models.
Top Episodes Quotes
- “When we think about building AI for robots, when we think about building foundation models for robots, we're thinking about really lifting robotics as a category from this former category of just being able to do repeated things to this category of really being able to handle diversity of environments, changes in the environments, and being able to understand what's around it and make intelligent decisions and actions to handle a diverse set of circumstances.“ by Peter Chen
- “If you want to fully recreate that in your simulation, that is actually more work than just learning a system that can deal with the real world.“ by Peter Chen
- “I would say the bar for the Chat GPT moment for robotics is higher. You need to solve the generality, which is the same kind of problem, but you need to solve it with high level of reliability.“ by Peter Chen
- “We have a simple carve out to this question, because we focus on industrial applications and well, all industrial robots have a set of safety rules that they need to conform to, because it's not just AI can be dangerous, like manual programming can be dangerous.“ by Peter Chen
- “There is one unique bet that we are placing. So that one unique bet is we believe the future of robotics would be built by whoever that has most robotics data.“ by Peter Chen
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Episode Information
No Priors: Artificial Intelligence | Machine Learning | Technology | Startups
Conviction | Pod People
1/25/24
Building adaptive AI models that can learn and complete tasks in the physical world requires precision but these AI robots could completely change manufacturing and logistics processes. Peter Chen, the co-founder and CEO of Covariant, leads the team that is building robots that will increase manufacturing efficiency, safety, and create warehouses of the future.
Today on No Priors, Peter joins Sarah to talk about how the Covariant team is developing multimodal models that have precise grounding and understanding so they can adapt to solve problems in the physical world. They also discuss how they plan their roadmap at Covariant, what could be next for the company, and what use case will bring us to the Chat-GPT moment for AI robots.
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Show Notes:
(0:00) Peter Chen Background
(0:58) How robotics AI will drive AI forward
(3:00) Moving from research to a commercial company
(5:46) The argument for building incrementally
(8:13) Manufacturing robotics today
(12:21) Put wall use case
(15:45) What’s next for Covariant Brain
(18:42) Covariant’s customers
(19:50) Grounding concepts in Ai
(25:47) How scaling laws apply to Covariant
(29:21) Covariant’s driving thesis
(32:54) the Chat-GPT moment for robotics
(35:12) Manufacturing center of the future
(37:02) Safety in AI robotics