# Robotyka i embodied AI

EN: **Robotics and embodied AI** · id `robotics` · aliases: robotics, embodied AI, robot learning, robotyka, AI ucieleśnione, uczenie robotów

Path: [AI Atlas](https://aiatlasgraph.com/llms.txt) › Robotyka i embodied AI

## Description

- PL: Dziedzina łącząca percepcję, planowanie, sterowanie i uczenie maszyn działających w świecie fizycznym: ramion, robotów mobilnych i humanoidów.
- EN: The field combining perception, planning, control and learning for machines acting in the physical world: arms, mobile robots and humanoids.

## In one sentence

- PL: To łączenie rozpoznawania otoczenia z ruchem i wykonywaniem zadań przez maszyny w świecie fizycznym.
- EN: This connects perception with movement and task execution by machines in the physical world.

## Problem it solves

- PL: Rzeczywiste przedmioty zmieniają położenie, pomiary są niedokładne, a wykonany ruch musi uwzględniać otoczenie i ograniczenia sprzętu.
- EN: Real objects change position, measurements are imperfect and movements must account for surroundings and hardware limits.

## What it does

- PL: Łączy obrazy i czujniki z wyborem oraz sterowaniem ruchów, ucząc zadania z pokazów, prób lub symulacji.
- EN: It connects images and sensors with action selection and movement control, learning tasks from demonstrations, trials or simulations.

## Everyday example

- PL: Robot widzi kubek na stole, dobiera chwyt i dostosowuje ruch ramienia do jego położenia oraz odczytów czujników.
- EN: A robot sees a cup on a table, chooses a grip and adjusts its arm movement to the cup's position and sensor readings.

## This concerns you if you…

- PL: chcesz, żeby maszyna działała w rzeczywistym otoczeniu
- PL: łączysz rozpoznawanie sytuacji z wykonywaniem ruchów
- EN: want a machine to act in real surroundings
- EN: connect situation recognition with physical movements

## Keywords

robot manipulation · inverse kinematics · motion planning · model predictive control · humanoid robots · legged locomotion · vision-language-action models · robot foundation models · imitation learning · sim-to-real · teleoperation · ROS 2 · MuJoCo · Isaac Lab · LeRobot

## How it works

- PL: Percepcja estymuje stan z sensorów, a planowanie wyznacza cel i sekwencję ruchów. Sterownik przelicza je na sygnały wykonawcze, wykorzystując feedback do korekty dynamiki oraz błędów obserwacji.
- EN: Perception estimates state from sensors, while planning defines objectives and movement sequences. A controller converts them into actuator signals, using feedback to correct dynamics and observation errors.

## Limits

- PL: Opóźnienia, kontakt i ograniczenia napędów wpływają na stabilność. Błędy percepcji mogą wywoływać błędy ruchu; modele uczone potrzebują kontroli wykonalności i testów sprzętowych.
- EN: Latency, contact and actuator limits affect stability. Perception errors can cause movement errors; learned models need feasibility checks and hardware testing.

## Key terms

- PL: **pętla sprzężenia zwrotnego**: Korygowanie sterowania na podstawie pomiaru skutków poprzednich działań.
- PL: **dynamika**: Zależność ruchu od sił, sterowania i stanu układu.
- PL: **stopnie swobody (DoF)**: Liczba niezależnych ruchów, które robot lub jego część może wykonać.
- EN: **feedback loop**: Correcting control based on measured effects of previous actions.
- EN: **dynamics**: How motion depends on forces, control and system state.
- EN: **degrees of freedom (DoF)**: The number of independent motions a robot or its part can make.

## Know first

- [Algebra liniowa: wektory i macierze (Linear algebra: vectors and matrices)](https://aiatlasgraph.com/t/linearalgebra.md)
- [Proces decyzyjny Markowa (MDP) (Markov decision processes (MDP))](https://aiatlasgraph.com/t/mdp.md)

## Examples

- PL: Manipulacja · nawigacja · sim-to-real
- EN: Manipulation · navigation · sim-to-real

## Subtopics (10 direct, 46 in total)

- [Dane i symulacja dla robotów (Robot data and simulation)](https://aiatlasgraph.com/t/robotdata.md): Data sources for robot learning—teleoperation, physics simulation and human recordings—and methods for transferring between them.
  - [Przenoszenie z symulacji do rzeczywistości (sim-to-real) (Sim-to-real transfer)](https://aiatlasgraph.com/t/simtoreal.md): Training in simulation and transferring to a real robot despite the differences (the “reality gap”), e.g. by randomising the simulation's appearance and physics.
    - [Cyfrowy bliźniak sceny do trenowania robota (real-to-sim) (Real-to-sim-to-real with scene digital twins)](https://aiatlasgraph.com/t/realtosim.md): Building a simulation from a real scene (reconstructing geometry, appearance and objects) and refining a policy there, e.g. with RL, before returning to hardware.
  - [Teleoperacja i zbiory demonstracji robotów (Teleoperation and robot demonstration datasets)](https://aiatlasgraph.com/t/teleopdata.md): Collecting robot-learning data: a human drives the robot (e.g. leader–follower arms) and the recordings go into shared datasets spanning many robot types.
  - [Testowanie polityk robota w symulacji i benchmarkach (Robot policy evaluation and benchmarks)](https://aiatlasgraph.com/t/robotevals.md): Evaluating manipulation and navigation policies: simulated task benchmarks, simulations mirroring real setups, and hardware test protocols.
  - [Uczenie robotów z nagrań ludzi (Learning robot skills from human videos)](https://aiatlasgraph.com/t/humanvideo.md): Pretraining visual representations on human video, often egocentric, to support later robot-manipulation learning.
- [Drony i roboty latające (Aerial robotics and autonomous drone flight)](https://aiatlasgraph.com/t/aerialrobotics.md): Autonomy for flying robots: state estimation from cameras and inertial sensors, flight planning and control, including policies learned in simulation.
  - [Samodzielna nawigacja drona w ciasnych miejscach (Vision-based autonomous drone navigation)](https://aiatlasgraph.com/t/dronenav.md): GPS-denied drone navigation from cameras and inertial sensors: localisation, mapping, obstacle-avoidance planning and learned policies.
  - [Szybki lot drona sterowany siecią neuronową (wyścigi dronów) (Learned agile flight and autonomous drone racing)](https://aiatlasgraph.com/t/agileflight.md): Flight policies learned by RL or imitation in simulation that issue low-level commands from onboard perception at speeds and accelerations near the drone's limits.
- [Floty i zespoły robotów (Multi-robot systems and fleet coordination)](https://aiatlasgraph.com/t/multirobot.md): Coordinating many robots: task allocation, collision-free multi-agent path planning, formation control and swarms, and fleet management.
  - [Planowanie tras wielu robotów bez kolizji (MAPF) (Multi-agent path finding (MAPF))](https://aiatlasgraph.com/t/mapf.md): Computing collision-free paths for many agents from starts to goals: search-based (CBS, PBS, LNS), compilation-based (SAT, MIP) and learned methods.
  - [Przydzielanie zadań robotom we flocie (Multi-robot task allocation (MRTA))](https://aiatlasgraph.com/t/mrta.md): Assigning tasks to robots to minimise time, energy or fleet size: exact methods, heuristics, auctions and learned policies.
- [Kinematyka i sterowanie robotem (Robot kinematics and control)](https://aiatlasgraph.com/t/robotcontrol.md): The classical core of robotics: describing motion geometry, planning collision-free trajectories and closed-loop actuator control.
  - [Kinematyka prosta i odwrotna (Forward and inverse kinematics)](https://aiatlasgraph.com/t/kinematics.md): The mapping between joint space and end-effector pose: direct one way, ambiguous and often iterative the other.
  - [Planowanie ruchu ramienia bez kolizji (Collision-free motion planning for manipulators)](https://aiatlasgraph.com/t/armmotionplanning.md): Searching for a collision-free path in an arm's configuration space with sampling-based methods or trajectory optimisation.
  - [Planowanie zadań i ruchów naraz (TAMP) (Task and motion planning (TAMP))](https://aiatlasgraph.com/t/tamp.md): Planning that combines discrete action choice (symbolic planning) with continuous motion planning and parameter selection, e.g. grasps and placements.
  - [Sterowanie siłą i podatnością (sterowanie impedancyjne) (Impedance, admittance and force control)](https://aiatlasgraph.com/t/impedancecontrol.md): Controlling the force–motion relationship: impedance (virtual spring-damper), admittance (force mapped to motion) and direct force control, including hybrid schemes.
  - [Sterowanie ze sprzężeniem zwrotnym (PID, MPC) (Feedback control for robots (PID, MPC))](https://aiatlasgraph.com/t/feedbackcontrol.md): Closed-loop controllers—from classical PID through LQR to model predictive control—that track a trajectory despite disturbances.
- [Lokomocja i roboty humanoidalne (Legged locomotion and humanoid robots)](https://aiatlasgraph.com/t/humanoids.md): Legged robots and humanoids: from model-based contact control to RL policies and human-motion imitation for loco-manipulation.
  - [Lokomocja robotów uczona w symulacji (Simulation-trained legged locomotion)](https://aiatlasgraph.com/t/locomotion.md): Walking policies for legged robots trained with RL across many parallel simulations and then transferred to physical hardware.
    - [Chodzenie po trudnym terenie z patrzeniem pod nogi (Perceptive legged locomotion on rough terrain)](https://aiatlasgraph.com/t/perceptivelocomotion.md): Locomotion policies fusing proprioception with terrain perception (height map, depth images), trained by RL in simulation and robust to unreliable external sensors.
  - [Naśladowanie ruchu człowieka przez robota (Motion imitation and retargeting from human data)](https://aiatlasgraph.com/t/motionimitation.md): Transferring motion from human recordings (motion capture, video) to robot kinematics and training RL policies that physically reproduce it.
  - [Sterowanie całym ciałem humanoida (Humanoid whole-body control)](https://aiatlasgraph.com/t/wholebodycontrol.md): Controlling all of a humanoid's joints at once with task priorities and contact constraints—via optimisation or a learned policy.
- [Manipulacja przedmiotami (Robotic manipulation)](https://aiatlasgraph.com/t/manipulation.md): The branch of robotics about physical interaction with objects: pose perception, grasp selection, contact-rich motion and learning manipulation skills.
  - [Douczanie robota próbami w realnym świecie z pomocą człowieka (Real-world reinforcement learning with human interventions)](https://aiatlasgraph.com/t/realworldrl.md): Reinforcement learning directly on a physical robot, initialised from demonstrations, with operator corrections during trials and reward from a success classifier.
  - [Manipulacja miękkimi przedmiotami (tkaniny, liny, kable) (Deformable object manipulation)](https://aiatlasgraph.com/t/deformablemanip.md): Perception, modelling and control for deformable objects (cloth, rope, cables, bags, food), whose state is very high-dimensional and whose dynamics are complex.
  - [Precyzyjne zadania z kontaktem (wkładanie, montaż, wycieranie) (Contact-rich manipulation (insertion, assembly, wiping))](https://aiatlasgraph.com/t/contactrich.md): Manipulation requiring controlled contact and force, sensitive to small position errors: insertion, assembly, screwing, wiping, polishing.
  - [Robot, który jeździ i chwyta naraz (manipulacja mobilna) (Mobile manipulation)](https://aiatlasgraph.com/t/mobilemanip.md): Combining navigation of a mobile base with one- or two-arm manipulation in a single task, using whole-body or hierarchical control.
  - [Wyznaczanie chwytu (Grasp detection and planning)](https://aiatlasgraph.com/t/grasping.md): Predicting stable gripper poses for an object from depth images or point clouds, analytically or with a learned model.
  - [Zręczna manipulacja dłonią robota (Dexterous in-hand manipulation)](https://aiatlasgraph.com/t/dexterousmanip.md): Controlling a multi-fingered hand to change an object's pose within the grasp, including with RL policies trained in simulation and transferred to hardware.
- [Modele bazowe dla robotów (Robot foundation models)](https://aiatlasgraph.com/t/robotfoundation.md): Pretrained models (VLA, LLM, VLM) used in robotics for perception, task planning and action generation across many platforms.
  - [Jedna polityka dla wielu typów robotów (Cross-embodiment robot policies)](https://aiatlasgraph.com/t/crossembodiment.md): Training policies on data from many platforms with different cameras, joints and action spaces, enabling transfer and quick fine-tuning.
  - [Modele wizja–język–akcja (VLA) (Vision-language-action models (VLA))](https://aiatlasgraph.com/t/vla.md): Robot models combining visual perception, language instructions and action generation, developed as generalist policies transferable across tasks and robots.
    - [Jak model zapisuje ruchy robota (tokeny i ciągi akcji) (Action tokenization and chunking in VLA models)](https://aiatlasgraph.com/t/vlaactions.md): Representing continuous robot motions in VLA models through discretisation, compression into tokens and prediction of trajectory chunks.
  - [Planowanie zadań robota z modelem językowym (Language-model task planning for robots)](https://aiatlasgraph.com/t/llmtaskplanning.md): Using language models to decompose instructions into sequences of robot skills or to write control code, grounded in the scene and capabilities.
- [Nawigacja robotów mobilnych (Mobile robot navigation)](https://aiatlasgraph.com/t/robotnav.md): Localisation, mapping and path planning for mobile robots, from the classical navigation stack to learned and language-guided navigation.
  - [Nawigacja według poleceń słownych (Vision-and-language navigation (VLN))](https://aiatlasgraph.com/t/vln.md): A task and family of methods in which an embodied agent navigates realistic interiors by natural-language instructions, evaluated on benchmarks such as R2R.
  - [Poruszanie się robota wśród ludzi (nawigacja społeczna) (Social robot navigation)](https://aiatlasgraph.com/t/socialnav.md): Planning mobile robot motion among pedestrians, accounting for trajectory prediction, social norms and comfort, together with evaluation methods.
  - [Szukanie przedmiotu w nieznanym budynku (nawigacja do obiektu) (Object-goal navigation)](https://aiatlasgraph.com/t/objectnav.md): An embodied navigation task: reach any object of a given category in an unseen interior using only egocentric observations.
- [Percepcja robota do działania (Robot perception for action)](https://aiatlasgraph.com/t/robotperception.md): Action-oriented perception: estimating 6D pose, affordances and contact state from images, depth, point clouds and tactile sensors.
  - [Rozpoznawanie, do czego służy przedmiot (afordancje) (Affordance learning for robot manipulation)](https://aiatlasgraph.com/t/affordances.md): Predicting possible actions on an object and the part that enables them (object–action–effect relation), from static data or interaction.
  - [Ustalanie położenia i obrotu przedmiotu (poza 6D) (6D object pose estimation and tracking)](https://aiatlasgraph.com/t/objectpose6d.md): Estimating and tracking an object's position and orientation (6 degrees of freedom) from RGB or RGB-D images, including objects unseen in training.
  - [Zmysł dotyku robota (czujniki dotykowe) (Tactile and visuotactile sensing for robots)](https://aiatlasgraph.com/t/tactilesensing.md): Measuring contact with tactile sensors (resistive, capacitive, visuotactile) and processing their signals into force, slip and shape.
- [Współpraca człowieka z robotem (Human-robot interaction and collaboration)](https://aiatlasgraph.com/t/hri.md): Research and techniques for robots interacting with people: physical contact safety, shared control, intention communication and language interaction.
  - [Bezpieczna praca ramię w ramię z robotem (koboty) (Safety in physical human-robot collaboration)](https://aiatlasgraph.com/t/cobotsafety.md): Modes and methods for safe physical collaboration: monitored stop, hand guiding, speed and separation monitoring, and power and force limiting.
  - [Wspólne sterowanie: człowiek prowadzi, robot pomaga (Shared autonomy and assistive teleoperation)](https://aiatlasgraph.com/t/sharedautonomy.md): Blending operator commands with robot autonomy under uncertainty about the user's goal, inferred online from their input history.

## Related topics

Untyped, symmetric "see also" links.

- [AI w grach (AI in games)](https://aiatlasgraph.com/t/gameai.md)
- [AI w przemyśle (Industrial AI)](https://aiatlasgraph.com/t/industrialai.md)
- [Estymacja pozy i punktów kluczowych (Pose and keypoint estimation)](https://aiatlasgraph.com/t/poseestimation.md)
- [Jednoczesna lokalizacja i budowa mapy (SLAM) (Simultaneous localisation and mapping (SLAM))](https://aiatlasgraph.com/t/slam.md)
- [Ukryte modele Markowa i filtrowanie (Hidden Markov models and filtering)](https://aiatlasgraph.com/t/hmm.md)

## Sources

- [Berkeley CS185/285 · Deep Reinforcement Learning (2026)](<https://rail.eecs.berkeley.edu/deeprlcourse/>) `rl` · reference: Uczenie przez wzmacnianie, imitacja, sterowanie i RL dla LLM.
- [MIT · Underactuated Robotics](<https://underactuated.mit.edu/>) `robotics` · reference: Sterowanie, dynamika i uczenie w robotyce.
- [Foundation Models in Robotics: Applications, Challenges, and the Future](<https://arxiv.org/abs/2312.07843>) `foundation-models-robotics-2023` · by R. Firoozi, J. Tucker, S. Tian, A. Majumdar i in.
- [Modern Robotics: Mechanics, Planning, and Control](<https://hades.mech.northwestern.edu/index.php/Modern_Robotics>) `modern-robotics-2017` · by Kevin M. Lynch, Frank C. Park
- [Robotic Manipulation: Perception, Planning, and Control (MIT course notes)](<https://manipulation.csail.mit.edu/>) `tedrake-manipulation` · by Russ Tedrake (MIT CSAIL)
- [Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning](<https://arxiv.org/abs/2501.02116>) `humanoid-hlm-survey-2026` · by Z. Gu, J. Li, W. Shen, W. Yu, Z. Xie i in. (m.in. C. K. Liu, X. B. Peng, Y. Zhu, G. Shi, Y. Zhao)

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AI Atlas revision 122 (2026-10-30) · HTML: https://aiatlasgraph.com/t/robotics · index: https://aiatlasgraph.com/llms.txt · everything: https://aiatlasgraph.com/llms-full.txt · updated 2026-10-30