Robot Learning Glossary: 60 Key Terms Defined

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A reference glossary covering imitation learning, policy architectures, data collection, hardware, and evaluation terms for robotics practitioners.

This glossary covers 60 key terms across six categories used in robot learning, teleoperation, and manipulation. Each definition is written for practitioners — concise, concrete, and with a brief example. For a more visual explanation of any concept, explore the rest of the Robotics Library.

Imitation Learning Terms

• Behavior Cloning (BC): The simplest imitation learning algorithm — train a supervised model to predict expert actions given states, treating it as a regression problem. Example: train a CNN to predict gripper position from camera images using 100 recorded pick-and-place demonstrations.
• DAgger (Dataset Aggregation): An iterative IL algorithm that fixes distribution shift by having the expert correct the learned policy's actions at states the policy actually visits. Example: deploy a BC policy, have an operator correct any mistakes in real-time, add corrections to training data, retrain.
• Action Chunking: Predicting K consecutive future actions at once rather than one action per step. Used in ACT (K=100). Example: the policy outputs 2 seconds of future arm motion at once, then re-plans — breaking the compounding error cycle.
• Distribution Shift: The mismatch between the state distribution seen during training (demonstrations) and the distribution visited at test time (robot execution). Example: the training data always starts with the object in a specific location; the robot fails when the object is slightly moved.
• Covariate Shift: A specific type of distribution shift where the input distribution (states) changes but the conditional output distribution (actions given states) is unchanged. Example: robot visits states not in training data because its own imperfect actions lead it off the demonstrated path.
• Compounding Error: The phenomenon where small per-step prediction errors accumulate geometrically over time, growing as O(T²ε) for horizon T and per-step error ε. Example: a 0.5% per-step error causes 25% cumulative error after 50 steps.
• Multi-modality: The property of a demonstration dataset where multiple distinct actions are valid for the same state. Example: a red block can be grasped from the left or the right — both are correct, but a single-mode model averages them into a bad middle grasp.
• Demonstration: A single recorded instance of a human expert performing a task that the robot should learn. Example: one complete recording of an operator picking up a block and placing it in a bin.
• Episode: One complete attempt at a task from start to finish, including all sensor data. May succeed or fail. Example: 150 seconds of synchronized image and joint data for one pick-and-place attempt.
• Rollout: An episode generated by the learned policy (rather than by a human). Used to evaluate and sometimes to collect additional training data. Example: deploying the trained BC policy for 20 test episodes to measure its success rate.

Policy Architecture Terms

• Transformer Policy: A policy implemented as a Transformer neural network — using self-attention to model temporal dependencies across timesteps or spatial dependencies across image patches. Example: ACT uses a Transformer decoder to generate action sequences conditioned on visual observations.
• CVAE (Conditional Variational Autoencoder): A generative model that learns a latent distribution conditioned on observed variables. Used in ACT to encode the multi-modality of demonstrations into a style variable. Example: ACT's CVAE encoder compresses the full action sequence into a latent code z, allowing the decoder to generate consistent action chunks.
• Diffusion Policy: A policy that models the action distribution as a denoising diffusion process — iteratively denoising random noise into a plausible action trajectory conditioned on observations. Example: Chi et al. 2023 demonstrated that Diffusion Policy outperforms BC on multi-modal tasks by representing multiple valid grasps simultaneously.
• Energy-Based Model (EBM): A model that assigns a scalar "energy" to each (state, action) pair; inference finds the action minimizing energy. Example: IBC trains an EBM where low energy = expert-like (state, action) pair; inference via gradient descent on action.
• Flow Matching: A generative modeling technique that learns to transport samples from a simple distribution to a complex target distribution via continuous normalizing flows. Example: π₀ (Physical Intelligence's foundation model) uses flow matching for action generation.
• Prediction Horizon: The number of future timesteps a policy predicts at once. Longer horizons enable smoother, more coordinated motion but require more accurate models. Example: a 50-step prediction horizon at 50 Hz covers 1 second of future motion.
• Chunk Size: The number of action steps in one prediction chunk (same as prediction horizon in ACT-style policies). Example: ACT uses chunk size K=100 at 50 Hz, so the arm plans 2 seconds of motion at a time.
• Action Head: The output module of a policy network that maps latent features to action values. Example: an MLP head taking Transformer decoder output and outputting 7 joint angles for a 6-DOF arm + gripper.

Data and Collection Terms

• Teleoperation: Human-controlled operation of a robot from a remote interface, typically used to collect demonstration data. Example: an operator wearing a VR headset moves their hands, and the robot arm mirrors those motions while all data is logged.
• Kinesthetic Teaching: A data collection method where the operator physically grasps and moves the robot arm (gravity-compensated, back-drivable mode) to demonstrate a task. Example: a researcher physically guides an arm through a peg-insertion task; joint angles are recorded at 100 Hz.
• Leader-Follower: A teleoperation architecture where the operator moves a lightweight replica arm (leader) and the robot arm (follower) mirrors the motion in real time. Example: ALOHA uses two leader arms (one per hand) to control a bimanual robot system.
• HDF5 (Hierarchical Data Format 5): A binary file format for storing large numerical datasets with chunked, compressed arrays. The standard storage format for robot episode data. Example: one HDF5 file per episode containing groups for /images, /joint_states, /actions, /metadata.
• RLDS (Reinforcement Learning Datasets): A TensorFlow Datasets-based format for robot learning data, standardizing episode and step structure. Used by Octo, Open X-Embodiment. Example: convert your HDF5 files to RLDS to train Octo on your custom data with minimal code changes.
• LeRobot: A HuggingFace-based robot learning framework and dataset format using Parquet files and a standardized schema. Example: publish your dataset to HuggingFace Hub in LeRobot format to share it with the community.
• Data Augmentation: Applying random transformations to training data to improve policy generalization. Example: randomly crop images by ±10%, jitter color by ±15%, add Gaussian noise to joint states during training.
• Success Rate: The fraction of evaluation rollouts in which the policy successfully completes the task. The primary evaluation metric for manipulation policies. Example: 18/20 successful grasps = 90% success rate.
• Rejection Rate: The fraction of collected episodes discarded during quality filtering. Example: collecting 500 episodes with 35% rejection rate yields 325 training episodes.
• Episode Length: The duration (in timesteps or seconds) of one episode. Example: mean episode length of 8 seconds at 50 Hz = 400 timesteps per episode.

Robot Hardware Terms

• DOF (Degrees of Freedom): The number of independent axes of motion in a robot arm. 6 DOF is the minimum for arbitrary end-effector poses; 7 DOF adds redundancy. Example: the UR5e has 6 revolute joints → 6 DOF.
• Payload: The maximum mass the robot arm can carry at its end-effector while maintaining rated performance. Example: a 5 kg payload cobot can reliably lift objects up to 5 kg including the gripper's own weight.
• Reach: The maximum radius from the robot's base that the end-effector can achieve. Determined by the sum of link lengths. Example: the UR5e has 850 mm reach — sufficient for a standard lab table setup.
• Repeatability: The precision with which the arm returns to the same commanded position on repeated attempts, measured as ±X mm. Example: ±0.05 mm repeatability means 200 repeated moves to the same target all land within a 0.1 mm diameter sphere.
• End-Effector: The device mounted at the tip of the robot arm that interacts with objects — the "hand." Example: a parallel jaw gripper, a suction cup array, or a multi-finger dexterous hand.
• Gripper: A specific type of end-effector that grasps objects by applying clamping force. Parallel jaw grippers are the most common for pick-and-place. Example: the Robotiq 2F-85 opens to 85 mm and closes with up to 235 N force.
• Force/Torque (F/T) Sensor: A six-axis wrist sensor measuring forces (Fx, Fy, Fz) and torques (Tx, Ty, Tz) at the end-effector. Example: detecting a successful grasp by verifying that gripper force exceeds 5 N with no slip torque.
• Tactile Sensor: A sensor that measures distributed contact pressure or geometry at the fingertip level. Example: GelSight produces a high-resolution image of finger contact geometry, enabling slip detection and grasp quality assessment.
• Joint Encoder: A sensor measuring the angle of a robot joint. Absolute encoders report the true angle without homing. Example: a 19-bit absolute encoder provides 0.0007° resolution at 1 kHz.
• Servo: In robotics, a self-contained motor+encoder+controller unit that accepts a position, velocity, or torque command. Example: Dynamixel servos are widely used in low-cost research arms — each servo handles its own PID control internally.

Learning Paradigms

• Reinforcement Learning (RL): A learning paradigm where an agent learns by taking actions and receiving scalar reward signals — no expert demonstrations needed. Example: training a grasping policy in simulation where the reward is +1 for successful lift and 0 otherwise.
• Offline RL: RL from a fixed dataset of pre-collected experience, without additional environment interaction. Example: training a policy from a robot dataset collected by humans using conservative Q-learning (CQL) to avoid out-of-distribution actions.
• Online RL: RL with active environment interaction — the policy collects new data and immediately learns from it. Example: a simulated robot repeatedly attempts grasps, updates its policy after each attempt, and gradually improves.
• Sim-to-Real: Training a policy in simulation and then deploying it on a real robot. The core challenge is the "reality gap" — simulation is never perfectly accurate. Example: training a locomotion policy in IsaacGym with randomized physics parameters, then deploying on a real quadruped.
• Domain Randomization: A sim-to-real technique that randomizes simulation parameters (friction, lighting, mass, texture) so the policy learns to be robust to these variations. Example: randomly varying table friction between 0.3–0.9 during training so the policy works on real tables with unknown friction.
• Foundation Model: A large neural network pre-trained on broad data (internet-scale or cross-robot datasets) that can be fine-tuned for specific tasks. Example: Octo pre-trained on 800K robot episodes from Open X-Embodiment, then fine-tuned on 50 task-specific episodes.
• VLA (Vision-Language-Action Model): A model that takes visual observations and natural language instructions as input and outputs robot actions. Example: OpenVLA accepts a camera image and the instruction "pick up the red cup" and outputs joint angle deltas.
• Fine-Tuning: Adapting a pre-trained model to a new task or environment by continuing training on a small task-specific dataset. Example: fine-tuning Octo on 200 custom pick-and-place episodes for 500 gradient steps.
• Zero-Shot: Applying a trained model to a new task or environment without any task-specific training. Example: a VLA trained on many tasks succeeds at a new instruction "pick up the blue block" without ever having seen that specific instruction during training.
• Few-Shot: Adapting to a new task using only a small number of examples (typically 1–20). Example: a meta-learning policy adapts to a new object shape after seeing just 5 demonstration episodes.

Evaluation Terms

• Success Rate: Fraction of evaluation trials where the policy completes the task. The standard primary metric. Example: 16/20 = 80% success rate over 20 real-robot evaluation trials.
• Out-of-Distribution (OOD): Inputs that differ from the training distribution — new object colors, positions, or environments not seen during training. Example: a policy trained on red blocks evaluated on green blocks is tested OOD on color.
• Generalization Gap: The difference between in-distribution and out-of-distribution success rates. Large gaps indicate overfitting to training conditions. Example: 90% in-distribution vs. 45% OOD = 45% generalization gap.
• Benchmark: A standardized set of tasks, objects, and evaluation protocols for comparing algorithms fairly. Example: FurnitureBench, RLBench, and LIBERO are common robot manipulation benchmarks.
• Real-World Evaluation: Policy evaluation on a physical robot (not simulation). Considered the gold standard; simulation metrics often do not transfer. Example: running 50 real-world trials on 5 different table setups to measure robustness.
• Ablation Study: An experiment removing or modifying one component of a system to measure its contribution. Example: ablating the CVAE component from ACT to measure how much it contributes to multi-task performance.
• Held-Out Set: A subset of data kept completely separate from training and only used for final evaluation. Ensures evaluation metrics are not inflated by overfitting. Example: reserving 10% of episodes from each task variant for evaluation, never exposing them to the training procedure.

For full-length explanations of any concept in this glossary, browse the RCSV Glossary or explore the Robotics Library articles above. This reference is updated as new techniques emerge in the field.