Hayden AI

Skild AI is developing a foundation model for robotics - a universal controller designed to operate across different robot morphologies including arms, humanoids, and quadrupeds. Founded in 2023 by Carnegie Mellon professors Deepak Pathak and Abhinav Gupta, the company is building systems that learn from observation and adapt to new robot bodies and environments without requiring task-specific training. The technical approach centers on massive simulation combined with deep learning to create what the company describes as physically-grounded intelligence. The architecture aims to enable robots to generalize across tasks and form factors, analogous to how large language models generalize across text domains. The company raised $300 million in Series A funding and has established partnerships with NVIDIA and Amazon. Target applications include manufacturing and logistics, where the ability to deploy a single model across different robot types could reduce the engineering overhead of task-specific programming. Technical domains span computer vision, deep learning, and robotics, with emphasis on simulation-based training methods that can scale across diverse robotic platforms. Skild AI applies foundation model techniques to the control problem in robotics, tackling the complexity of physical interaction, real-time control constraints, and the reality gap between simulation and deployment. The company is research-driven, focused on moving algorithms from academic settings into physical systems that operate in industrial environments.

We're Gatik, and we're building autonomous trucks that handle the middle-mile logistics for some of North America's biggest retailers. Instead of chasing long-haul trucking or robotaxis, we focused on the regional routes between distribution centers and stores - where goods move frequently but predictably. This is where our Level 4 autonomous system, Gatik Driver™, really shines. It's built specifically for fixed, repeatable routes that retailers depend on every single day, making it possible to remove the driver entirely while keeping operations safe and efficient. What makes us different is that we're not running experiments or small pilots. Our trucks are commercially deployed right now, moving freight for Fortune 50 companies like Walmart, Kroger, and Loblaw across multiple states. We've completed over 60,000 fully driverless deliveries without an incident, and we're the first company in the U.S. operating driverless trucks at commercial scale. Our team combines expertise in autonomous systems with a pragmatic approach to solving real supply chain problems - no hype, just reliable freight that keeps shelves stocked and businesses running smoothly.

Robust AI develops collaborative mobile robotics solutions for warehouse and manufacturing environments, with an emphasis on human-centric design and AI-powered workflow integration. The company was founded by a team that includes robotics pioneer Rodney Brooks and is currently led by CEO Anthony Jules. Their engineering approach prioritizes robots that work alongside human workers in material handling and logistics operations rather than replacing them. The company's flagship product, Carter, is a collaborative mobile robot designed for rapid deployment in dynamic warehouse and manufacturing settings. The system is offered as an all-inclusive, no-CapEx solution that requires zero infrastructure changes to existing facilities. Carter integrates AI-powered workflows to operate in environments where adaptability to changing conditions is essential. The platform has been deployed at DHL, where the company reports 60%+ productivity gains, and Robust AI has established a partnership with Foxconn to scale manufacturing applications. Robust AI's technical focus spans collaborative mobile robotics, artificial intelligence, and automation for material handling and logistics. The engineering philosophy centers on making robots broadly useful and effortless to adopt, addressing the practical constraints of deploying autonomous systems in production environments where human-robot collaboration is a requirement rather than an edge case. The company positions its solutions for immediate ROI through deployment models that minimize upfront capital investment and infrastructure modification.

In 2025, Zhou Xian and Théophile Gervet recognized a fundamental bottleneck in the robotics field: while digital AI had made extraordinary progress, physical AI - the intelligence enabling machines to perceive, understand, and interact with the real world - lagged dramatically behind. Physical labor contributes an estimated $30-40 trillion to global GDP, yet over 95% remains unautomated because current robotic systems rely on brittle, rigid, and overfitted software stacks that are narrow in scope, costly to deploy, and impossible to scale. They founded Genesis AI to bridge this gap, assembling a team of world-class talent from Mistral AI, NVIDIA, Google, CMU, MIT, Stanford, Columbia, and UMD. Their vision: build a universal robotics foundation model and horizontal robotics platform that would unlock unlimited physical labor. Genesis would achieve this through a scalable data engine fusing real-world robot interaction with high-fidelity physics simulation, an open-source ecosystem to accelerate field-wide progress, and robust robot deployments thriving in messy, unconstrained environments. With $105 million in seed funding from Eclipse Ventures and Khosla Ventures, Genesis emerged from stealth in July 2025 to build what's next in physical AI.

At Moonvalley, we believe the future of filmmaking lies at the intersection of cutting-edge AI research and creative vision. Our team of elite researchers from DeepMind, Google, Microsoft, Meta, and Snap works alongside accomplished filmmakers to build generative AI models that meet Hollywood standards. We don't just build technology - we've established the first AI-enabled movie studio, partnering with top producers, actors, and global brands to push the boundaries of what's possible in visual storytelling. We're committed to a fundamentally different approach: our Marey model is trained exclusively on licensed, owned data, ensuring commercial safety and ethical AI practices that filmmakers can trust. Our culture is one of unprecedented convergence, where deep learning experts and creative talent collaborate daily to solve problems that have never been solved before. We approach our work with the dedication of Olympic athletes - anticipating intense commitment, late nights, and weekends dedicated to our mission. If you're driven by seemingly impossible technical challenges and want to build technology that redefines creativity, Moonvalley is where you'll find your people.

Radical AI operates autonomous laboratories that combine machine learning, robotics, and closed-loop experimentation to discover inorganic materials for aerospace, clean energy, and semiconductor applications. The company's self-driving laboratory system executes synthesis and characterization experiments without human intervention, feeding results back into AI models that screen billions of material compositions to identify candidates for physical validation. This architecture transforms materials R&D from sequential workflows into parallel feedback loops where prediction, synthesis, and performance measurement operate as a continuous integrated system. The technical stack spans Python and C++ for core systems, ROS/ROS2 for robotic control, computer vision and SLAM for laboratory automation, and reinforcement learning for adaptive experimental design. Simulation environments include MuJoCo and IsaacSim for developing manipulation and navigation behaviors before hardware deployment. Infrastructure uses gRPC/protobuf for inter-service communication, OpenTelemetry and Prometheus for instrumentation, with Grafana for monitoring closed-loop experiment execution at scale. With $55M in Series Seed+ funding, the company addresses multi-decade materials challenges in industries where novel inorganic compounds enable next-generation performance - higher-temperature aerospace alloys, more efficient energy conversion materials, and advanced semiconductor substrates. The approach grounds AI prediction in physical constraints: every screened composition must survive automated synthesis protocols, pass characterization under real operating conditions, and demonstrate reproducibility across experiment cycles before qualifying as a discovery.