Models & learning
Transfer learning
Transfer learning uses knowledge learned from one or more source tasks or domains to improve learning or performance on a different target task or domain. In robotics, transfer may reuse representations, model weights, skills, data, or policies across robots, tasks, environments, sensing configurations, or simulation and reality.
Also known as: knowledge transfer, machine-learning transfer
Updated
Transfer connects a source and a target
Pan and Yang frame transfer learning in terms of source and target domains and tasks. The distinction matters: adapting the same grasping task from simulation to reality is different from reusing a vision representation for a new manipulation task.
Robotics systems transfer pretrained visual features, language representations, dynamics knowledge, motion primitives or complete policies. Fine-tuning is one mechanism, not a synonym for the whole field; transfer can also use frozen features, adapters, shared latent spaces or explicit mappings between embodiments.
Transfer can help or harm
Positive transfer improves the target relative to learning without the source knowledge. Negative transfer occurs when mismatched source assumptions reduce performance. A policy trained on a fixed-base arm may encode reaches or camera geometry that do not carry safely to a mobile humanoid.
Similarity should be stated rather than assumed. Compare action spaces, observations, morphology, controller behaviour, object distributions and success criteria. More source data is not automatically useful if it reinforces the wrong invariances.
Evaluation needs a target-only baseline
Claims of transfer should identify what moved from source to target and what target data remained necessary. Compare against training from scratch or another target-only baseline under the same data and compute budget.
For datasets, retain embodiment and domain metadata so source subsets can be separated. Report target performance across several seeds and conditions, not only the best checkpoint. Transfer is demonstrated by measurable target benefit, not merely by loading pretrained weights.
Sources
Related terms
Simulation & transfer
Domain adaptation
Domain adaptation is a form of transfer learning that learns to perform a task in a target domain using knowledge from a related source domain whose data distribution differs. The task is typically shared or closely related, while observations, dynamics, environments, sensors, or other domain conditions change.
Simulation & transfer
Sim-to-real
Sim-to-real is the transfer of a model, policy or behaviour developed wholly or partly in simulation to a physical robot or real environment. The central problem is the reality gap: errors in simulated dynamics, sensing, appearance and timing can make a strategy successful in simulation but unreliable or unsafe on hardware.
Data & collection
Cross-embodiment data
Cross-embodiment data is robot training data drawn from multiple physical embodiments, such as arms, mobile manipulators, quadrupeds or humanoids with different kinematics, sensors and action spaces. The datasets are aligned or packaged so models can learn jointly from experience produced by different robots, although shared formatting does not make their observations or controls physically equivalent.
Models & learning
Robot generalization
Robot generalization is a policy's ability to perform a learned behavior when test conditions differ from training, such as with new objects, environments, tasks, or robot embodiments. A useful claim specifies which conditions were held out.
Models & learning
Robot foundation model
A robot foundation model is a broadly pretrained model intended to provide a reusable starting point for multiple robot tasks, environments or embodiments. It learns from diverse robotics and sometimes web or human data, then acts directly or is adapted with target-domain data. The term describes a training and reuse strategy, not one fixed architecture.