[{"content":"About Physical AI Lab Physical AI Lab exists because robots are finally interesting to a much wider audience, but the public story is uneven.\nOne video makes a humanoid look ready for every home. A warehouse tour makes mobile robots look effortless. A dexterous hand demo makes general manipulation look solved. Then a real deployment meets stairs, clutter, transparent packaging, reflective floors, messy cables, variable lighting, humans walking through the task, maintenance schedules, battery limits, regulations, and a business case.\nThis site keeps those layers separate.\nWhen a robot can do something reliably in a bounded environment, we say so. When a capability is mostly a pilot, demo, or research direction, we say that too. When a product category is useful but narrow, we do not pretend it is a general-purpose servant.\nThe tone here is practical because physical AI is physical. Mistakes are not just wrong text. They can break inventory, drop objects, block aisles, damage property, or hurt people. Useful robotics needs sensors, controls, software, maintenance, training, risk assessment, fallback behavior, and a clear answer to \u0026ldquo;what happens when this goes wrong?\u0026rdquo;\nStart with What Robots Can Actually Do if you want the grounded overview.\n","contentType":"physical-ai-lab","date":"0001-01-01","permalink":"/physical-ai-lab/about/","section":"physical-ai-lab","site":"Fondsites","tags":null,"title":"About Physical AI Lab"},{"content":"Contact Physical AI Lab If you have a correction, research lead, deployment note, safety reference, or guidebook topic we should cover next, send it over.\nRobotics changes quickly, but durable explanations still matter. Useful messages are specific: the claim, the source, what seems wrong, and how you think it should be framed.\nReach out Email: contact@fondsites.com\nEspecially useful messages Corrections If a guide overstates what a robot can do, understates a risk, or blurs the difference between a demo and a production deployment, tell us.\nField notes If you work with AMRs, cobots, mobile manipulators, home robots, service robots, humanoids, end-effectors, robot safety, simulation, or fleet operations, concrete field notes are welcome.\nResearch pointers Send readable papers, standards, public datasets, safety documents, product manuals, or talks that helped you understand a piece of the physical AI stack.\nFor now, head back to Physical AI Lab or open the guidebook shelf .\n","contentType":"physical-ai-lab","date":"0001-01-01","permalink":"/physical-ai-lab/contact/","section":"physical-ai-lab","site":"Fondsites","tags":null,"title":"Contact Physical AI Lab"},{"content":"","contentType":"physical-ai-lab","date":"0001-01-01","permalink":"/physical-ai-lab/games/","section":"physical-ai-lab","site":"Fondsites","tags":null,"title":"Physical AI Lab Game Lessons"},{"content":"Robots are easiest to understand when you stop asking whether they are \u0026ldquo;smart\u0026rdquo; and start asking what world they are built for.\nA warehouse robot has a map, a fleet manager, marked workflows, known payloads, and trained operators. A home robot has pets, thresholds, toys, chair legs, clutter, privacy concerns, and no facilities team. A humanoid has the attractive promise of fitting human spaces, but also the cost and control burden of legs, arms, balance, hands, perception, and safety all at once.\nThis shelf is built around those differences.\nReading path What Robots Can Actually Do Humanoid Robots: The Practical Guide Robot Hands and Dexterous Manipulation Home Robots: Useful, Narrow, and Hard Warehouse Robots: AMRs, Arms, and Real Workflows Embodied AI: Models That Meet the World Robot Autonomy: The Stack Behind the Demo Robot Safety: Risk, Standards, and Good Boundaries Capability Cluster What Robots Can Actually Do Embodied AI Robot Autonomy Platform Cluster Humanoid Robots Robot Hands and Dexterous Manipulation Warehouse Robots Home Robots Safety Cluster Robot Safety Warehouse Robots Home Robots The short version The robots that work best today usually have one or more advantages: constrained environments, repeatable tasks, known objects, engineered workflows, trained users, simple success metrics, and safe fallback states. The harder the setting becomes, the more the robot needs perception, manipulation, reasoning, safety design, maintenance, and honest limits.\n","contentType":"physical-ai-lab","date":"0001-01-01","permalink":"/physical-ai-lab/guidebooks/","section":"physical-ai-lab","site":"Fondsites","tags":null,"title":"Physical AI Lab Guidebooks"}]