iColo: A Digital Realty Company’s Post

Autonomous work machines need to know the right actions in arbitrary states and situations as they perform tasks. Realistic simulation environments and self-learning control agents can cover the state gaps to master control schemes in complex situations. #autonomousvehicles #autonomousworkmachines #mobileworkmachines #simulation #simulations #syntheticdata #simulationenvironment #selflearning #control #agents #controlagents #situationalawareness #AI #artificialintelligence #ML #machinelearning https://lnkd.in/dpVhWCeV

First fully autonomous pick and place model that I trained using LeRobot and the Koch arm.

Understanding Planning Techniques in Autonomous Agents: https://lnkd.in/exHyeNR9 #artificialIntelligence #machineLearning #LLMs #generativeAI

Thrilled to share insights on how AI-driven autonomous research is revolutionizing the field. From accelerating data analysis to uncovering deeper insights, AI is transforming the way we approach research and innovation. This is a game-changer for industries aiming to stay ahead of the curve and make data-driven decisions with unprecedented accuracy and speed. Kudos to the teams and innovators leading this charge! 🌟 #AI #AutonomousResearch #Innovation #DataDriven #FutureOfResearch

This pick-and-place policy controlling an Alexander Koch open-source arm was trained using visual inputs from two cameras and 50 demonstrations. To clarify, achieving similar results with Reinforcement Learning—whether model-free or model-based, value-based or policy-gradient-based, or using Actor-Critic methods—typically requires tens of thousands of trial-and-error iterations. As demonstrated by alignment techniques in Large Language Models (LLMs), success does not come from a one-size-fits-all "silver bullet" approach but from the effective integration of demonstrations and real-world feedbacks, which drives efficient convergence toward intelligent behavior. The Hugging Face LeRobot Library enables such research to be conducted in an open, accessible, and reproducible manner, engaging a noticeable paradigm shift in robotics.

While many dream of the convenience #autonomousdriving will bring, developers need to put safety first. This is why Imagry uses supervised learning techniques to train its #AI systems.

Artificial intelligence will undoubtedly play a role in the future of transportation. The question is, how? According to a notice from the White House, "virtually all aspects of transportation and mobility... will likely be impacted by the deployment of AI tools and applications." The U.S. Department of Transportation is seeking public input as the government decides how to apply this innovative new technology to a critical sector of our country's infrastructure. Specifically, the US DOT is seeking feedback on autonomous driving and safety considerations. Learn more about this and see how to participate: https://lnkd.in/eu63RaXc

Automation is one of the questions for this age. For the last few years, I have published and lectured on what I call the “Automation Paradox”—efforts to marry existing legal principles with previously unknown autonomous technology. I recently lectured on this paradox in Boston and Tulsa dealing with self-driving cars and generative AI. This is a draft of those lectures. #generativeAI; #autonomous; #ChatGPT; #selfdrivingcars; Tulsa Law Review

NEW! Formal Methods for Safe Autonomy https://bit.ly/3YY65k7 introduces new verification and synthesis algorithms to provide certifiable trusts for real-world autonomous systems. There are significant financial and legal implications for ensuring design correctness and safety in autonomous systems. Insights throughout the book provide a level of assurance that can be provided by formal methods for today’s autonomous systems. Author: Chuchu Fan, Massachusetts Institute of Technology. #autonomous #systems #verification #synthesis #algorithms