Robotyka i embodied AI

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

Dziedzina łącząca percepcję, planowanie, sterowanie i uczenie maszyn działających w świecie fizycznym: ramion, robotów mobilnych i humanoidów.

The field combining perception, planning, control and learning for machines acting in the physical world: arms, mobile robots and humanoids.

In one sentence

To łączenie rozpoznawania otoczenia z ruchem i wykonywaniem zadań przez maszyny w świecie fizycznym.

This connects perception with movement and task execution by machines in the physical world.

Problem it solves

Rzeczywiste przedmioty zmieniają położenie, pomiary są niedokładne, a wykonany ruch musi uwzględniać otoczenie i ograniczenia sprzętu.

Real objects change position, measurements are imperfect and movements must account for surroundings and hardware limits.

What it does

Łączy obrazy i czujniki z wyborem oraz sterowaniem ruchów, ucząc zadania z pokazów, prób lub symulacji.

It connects images and sensors with action selection and movement control, learning tasks from demonstrations, trials or simulations.

Everyday example

Robot widzi kubek na stole, dobiera chwyt i dostosowuje ruch ramienia do jego położenia oraz odczytów czujników.

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…

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

Examples

Manipulacja · nawigacja · sim-to-real
Manipulation · navigation · sim-to-real

Subtopics

Dane i symulacja dla robotów (Robot data and simulation): 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): 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): 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): 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): 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): Pretraining visual representations on human video, often egocentric, to support later robot-manipulation learning.
Drony i roboty latające (Aerial robotics and autonomous drone flight): 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): 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): 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): 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)): 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)): 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): 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): 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): 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)): 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): 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)): 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): 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): 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): 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): 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): 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): 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): 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): 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)): 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): 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): 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): 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): 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): 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)): 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): 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): 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): 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)): 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): 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): 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): 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): 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): 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): 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): 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): 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): Blending operator commands with robot autonomy under uncertainty about the user's goal, inferred online from their input history.

Sources

Berkeley CS185/285 · Deep Reinforcement Learning (2026) · reference: Uczenie przez wzmacnianie, imitacja, sterowanie i RL dla LLM.
MIT · Underactuated Robotics · reference: Sterowanie, dynamika i uczenie w robotyce.
Foundation Models in Robotics: Applications, Challenges, and the Future · R. Firoozi, J. Tucker, S. Tian, A. Majumdar i in.
Modern Robotics: Mechanics, Planning, and Control · Kevin M. Lynch, Frank C. Park
Robotic Manipulation: Perception, Planning, and Control (MIT course notes) · Russ Tedrake (MIT CSAIL)
Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning · 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)