manipulation

Robotics

The branch of roboticsrobotics, n.: The science and engineering of building robots and making them work: designing their bodies, sensors and motors, and writing the software that lets them see, plan and move safely. concerned with how robots physically interact with objects to change their position, orientation or state — picking and placing, pushing, inserting, opening doors and drawers, folding cloth, assembling parts and using tools — usually with an arm-type manipulator ending in an end effectorend effector, n.: The tool at the end of a robot arm that actually touches the world, such as a gripper, suction cup, welding torch, paint sprayer or robotic hand.. Classical manipulation splits the problem into perceptionperception, n.: How an AI system or robot turns raw sensor signals, such as camera pixels, microphone sound or laser scans, into an understanding of what is around it: objects, people, words and distances. (estimating object pose), graspgrasping, n.: How a robot picks up and holds an object securely with a gripper or hand: deciding where to put its fingers or suction cup and how hard to squeeze. selection, motion planningmotion planning, n.: Working out how a robot should move from where it is to where it needs to be, without hitting anything and within the limits of what its body can do. and inverse kinematicsinverse kinematics, n.: Working backward from where you want a robot's hand to be to the joint angles that will put it there, like working out how to bend your arm to touch your nose., and control, with force or impedance controlimpedance control, n.: A way of controlling a robot so that it behaves like a spring and shock absorber when it touches things, giving way gently instead of pushing rigidly. for contact-rich phases such as insertion. Contact is what makes manipulation hard: friction, slip, deformable and articulated objects, and occlusion by the robot's own hand make outcomes difficult to model and predict. Learned approaches — imitation learningimitation learning, n.: Teaching an AI by showing it examples of an expert doing a task, so it learns to copy the behavior instead of discovering it through trial and error. from teleoperatedteleoperation, n.: Controlling a robot or machine from a distance, with a person steering it in real time using a joystick, a matching 'puppet' arm, a VR headset or a screen. demonstrations, reinforcement learningreinforcement learning, n.: A way of teaching a computer program by trial and error: it tries actions, earns points for good results and penalties for bad ones, and gradually learns which choices pay off over time., diffusion policiesdiffusion policy, n.: A way of training robots in which the same kind of AI that generates images instead generates the robot's next movements, letting it copy demonstrations even when there are several good ways to do a task. and vision-language-action modelsvision-language-action model, n.: An AI model that looks through a robot's cameras, reads an instruction such as 'put the cup in the sink', and turns both straight into the robot's movements. — have widened what robots can handle, but reliability in unstructured homes remains a central open problem. Variants include mobile manipulation (an arm on a moving base), bimanual manipulation and dexterous manipulationdexterous manipulation, n.: Skilled handling of objects with robot fingers, such as spinning a pen, turning a key or rotating a cube in one hand: the fine finger work people do without thinking. with multi-fingered hands.

Navigation is mostly solved in our warehouse; manipulation of floppy polybags is the bottleneck.