The DiscoveryLayer forPhysical AI
Exploring the machines, technologies and interactions shaping the physical world.
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Featured
What Is Robot Control? From High-Level Commands to Physical Motion
Robot control turns desired behavior into physical action through feedback, motion and force regulation, predictive optimization, whole-body coordination, and learned components.
Read article →Robotics
Robotics
Robotics technology, companies, products, and commercialization.
What Is a World Model in Robotics? How Robots Predict What Happens Next
World models help robots predict how actions may change the physical world. This explainer covers learned dynamics, planning, latent prediction, simulation, VLAs, and the limits of predicting real-world outcomes.
EvergreenWhat Is a Robot Foundation Model? From AI Models to Physical Intelligence
Robot foundation models extend the foundation-model idea into physical systems, where data, perception, action, embodiment, and feedback must work together under real-world constraints.
EvergreenHow Does Robot Manipulation Work? From Perception and Planning to Control
Robot manipulation is purposeful physical interaction with objects and environments. This guide uses peg insertion to explain task-relevant state, grasping, planning, contact, control, feedback, learning-based methods, and the evidence needed beyond a lab demonstration.
EvergreenWhat Is Sim2Real in Robotics? How Robots Move from Simulation to Reality
Sim2Real describes the methods and evidence used to move a robot capability from a simulated environment to a physical one. This guide explains the reality gap, transfer strategies, and why simulation success alone is not deployment evidence.
EvergreenHow Do Robots Learn? From Demonstrations and Reinforcement Learning to Sim2Real
Robot learning is how a robot acquires or improves a capability from data, demonstrations, interaction, or simulation. This article explains the role of behavior cloning, reinforcement learning, robot datasets, and Sim2Real, while separating learned policies from planners, controllers, and physical deployment evidence.
EvergreenWhat Is Embodied AI? How Intelligence Learns Through Body, Environment, and Action
Embodied AI is a system-level approach to intelligence in which an agent’s body and environment shape what it can perceive, learn, and do. This guide defines the term, separates it from adjacent model categories, and explains why physical interaction creates different data, control, and safety challenges.
Topics
Topics
Enter the knowledge base through maintained topics connected to articles and companies.
Humanoid Robots
A guide to humanoid-robot systems, capabilities, industry participants, and commercialization.
Physical AI
How AI perceives, decides, and acts in the real world through robots and other physical systems.
Robot Learning
How robots acquire executable capabilities from demonstrations, data, simulation, and interaction.
Robot Manipulation
How robots grasp, move, assemble, and use tools to change the physical environment.
Vision-Language-Action (VLA)
How vision-language-action models turn observations and natural-language instructions into robot actions.
Companies
Recently Updated Companies
Localized company profiles ordered by their latest verification or content update.
Tesla Optimus
Tesla is connecting its vehicle-scale manufacturing and AI infrastructure to the Optimus humanoid program; the public record is stronger on prototypes and internal testing than on external commercial deployment.
Updated Aug 26, 2026US · SalemAgility Robotics
A Salem-based robotics company developing Digit for logistics, manufacturing, and other industrial workflows.
Updated Aug 26, 2026US · AustinApptronik
Apptronik is a U.S. robotics company developing the Apollo humanoid platform for manufacturing, logistics, and warehouse work.
Updated Aug 26, 2026US · San JoseFigure AI
A San Jose AI robotics company building general-purpose humanoid robots and the Helix vision-language-action system, with Figure 03 as its current generation.
Updated Aug 24, 2026