Topics
Robot Manipulation
How robots grasp, move, assemble, and use tools to change the physical environment.
Robot manipulation studies how robots make contact with objects and change their surroundings through grippers, dexterous hands, arms, or tools. It includes grasping, placing, assembly, tool use, and bimanual coordination. Unlike locomotion alone, manipulation depends on object-state estimation, contact dynamics, motion planning, force control, and recovery from errors.
This topic connects perception, control, touch, learning, and hardware design. It focuses on whether systems remain reliable across object variation, environmental disturbance, and long task sequences rather than succeeding once under controlled conditions.
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EvergreenWhat 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.
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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 a Vision-Language-Action Model?
A clear guide to how Vision-Language-Action models connect language, vision, and robot behavior—and where they fit within a larger control stack.
EvergreenFrom LLMs to Robot Motion: How VLMs, VLAs, World Models, RL, Sim2Real and Control Fit Together
A practical guide to the Physical AI stack: how language and vision become actions through VLA policies, planning, world models, learning, simulation, Sim2Real and robot control.