Ai

The integration of AI into robotics has transformed how robots and autonomous systems perceive, learn, and act across industries: from intelligent bin-picking to collaborative tasks in modern factories. Now, with the rise of Generative AI, we’re witnessing a fundamental shift in how robotics systems are built, trained, and deployed.

This talk will discuss how to develop intelligent robotic systems using both traditional AI approaches and the latest advancements in robotics foundation models. Learn how to design end-to-end workflows that incorporate deep learning, reinforcement learning, transformer-based vision-language-action (VLA) models all within a single, simulation-driven platform.

Highlights:
• Design and deploy AI-powered bin-picking and motion planning systems with reduced human supervision.
• Automate data labeling and training for object detection and pose estimation.
• Object detection with zero-shot text-conditioned models.
• Segmenting objects across images and videos using vision foundation models

As robotics shifts from task automation to intelligent autonomy, the real challenge isn’t just building robots that move, it’s designing systems that learn, adapt, and earn trust at scale. Kavitha Velusamy, senior vice president of software and AI at Brain Corp, will unpack what it takes to build and deploy robot fleets that combine reliability with intelligence.

Drawing from Brain Corp’s experience powering over 40,000 robots worldwide, Kavitha will explore how new approaches in perception, data integration, and continuous learning are redefining autonomy in real-world environments like retail, logistics, and public spaces. She’ll share how teams can bridge the gap between simulation and deployment, optimize for safety and privacy by design, and architect systems that evolve responsibly over time.

Attendees will gain a behind-the-scenes view of how to turn complex, dynamic environments into structured intelligence, and practical lessons on scaling robot software, managing edge-to-cloud data flows, and maintaining trust with users and regulators alike.

As your robotics software development organization scales, the developer experience grows both more important and harder to get right–and an inefficient build system can cost you in slow iteration cycles, developer frustration, and wasted resources.

We will share insights from the Robotics and AI Institute’s DevExp and build system overhaul: a project with a scope spanning from repository organization (mono-repo or multi-repo?) to polyglot build systems with caching and remote build execution, ROS integration, management of (conflicting!) dependencies, all the way to runtime tools, deployment, and migration–for an organization consisting of many different robotics research and engineering teams with diverse needs.

We’ll discuss our process, the choices we made and why, as well as other options we considered, bespoke tools we had to build, and retrospective lessons and future plans. Come to learn how we’re saving 164 CPU-hours per day, how we make Bazel and ROS work together, or how close we came to total failure before delighting users.

Robots don’t get smarter by accident; they improve through a deliberate data flywheel. This panel explores how real-world robot data is captured, curated, and fed back into perception, autonomy, and system performance. Drawing on lessons from field-deployed robots, we’ll explore how to design the data flywheel from day one, turning pilots into scalable, field-ready platforms that continuously learn and improve in production.

Robots are evolving from programmed ROS applications to embodied AI (LLMs running on physical robotics hardware) solving missions. New robotics AI tools are evolving such as VLMs (Vision Language Models also called multimodal LLMs), VLAs (Vision Language Action models), and MCP (Model Context Protocol) servers. Robots now stream sensors like RealSense depth cameras into these AI tools allowing the AI to figure out on its own how to move its wheels or legs to achieve a goal such as following a person. During this session, we will build an embodied AI experience together live on stage! What could go wrong?!