Simulation & transfer
System identification
System identification is the process of estimating a dynamical model from measured inputs and outputs. In robotics it can fit parameters of a known mechanical model or learn a model structure that predicts how a robot or environment responds to actions.
Also known as: robot system identification, dynamical system identification
Updated
Learn the response, not just the current state
A state estimator asks where the robot is and how it is moving. System identification asks which model explains its response to inputs. The problems can interact, but a joint-angle estimate is not itself an identified actuator or rigid-body model.
Tedrake's system-identification notes distinguish fitting parameters when the mechanical structure is known from learning more general input-output or state-space models. On a humanoid, structured identification might estimate combinations of inertial parameters, damping or joint friction. It is not restricted to fitting a simulator, although identified parameters can improve one.
More recordings do not make every parameter identifiable
Different parameter values can produce indistinguishable measured behaviour. The MIT notes explain that robot dynamics often reveal only identifiable combinations of parameters, not every physical parameter independently. They also discuss experiment design: the recorded motions must excite the effects being estimated, rather than repeatedly collecting the same uninformative trajectory.
For example, fitting a joint model only from near-stationary recordings is not a convincing validation of its predictions during rapid motion. This is a data-selection implication, not a claim that aggressive excitation is always appropriate. Identification experiments still need hardware limits, contact conditions and safe operating bounds.
Sensor calibration and timestamp alignment matter because biased or misaligned measurements can be absorbed into the fitted dynamics. Preserve the original measurements and document any filtering, derivative estimation and conversion from motor signals to joint effort.
Validate rollouts as well as one-step predictions
Tedrake distinguishes equation error from simulation error: a model can fit one-step transitions well yet accumulate large errors when its own predictions are fed forward. Evaluation should therefore include held-out trajectories and prediction horizons relevant to the intended controller, not just training loss.
System identification also differs from domain randomisation. Identification fits measured behaviour; randomisation trains or tests across a distribution of conditions. The methods can be combined by centring variation around an identified model while retaining uncertainty that the data did not resolve.
A useful identification dataset records commands, measured response, timing, units, operating conditions and the train/validation split. Publish the model's intended regime alongside its parameters: a fit for an unloaded joint is not evidence that the same model captures every payload, temperature or contact condition.
Sources
Related terms
Hardware & control
Robot dynamics
Robot dynamics describes the relationship between forces and torques acting on a robot and the motion they produce, accounting for mass, inertia, gravity, velocity-dependent effects and external contact. Forward dynamics predicts acceleration from applied forces; inverse dynamics computes forces or torques for a specified motion.
Data & collection
Sensor calibration
Sensor calibration is the estimation and documentation of parameters that map raw sensor readings into physically meaningful values and known spatial relationships. In robotics it can include scale, bias, distortion, intrinsic camera parameters, sensor-to-sensor or sensor-to-robot transforms, and timing offsets. Calibration does not remove all noise or drift.
Hardware & control
State estimation
State estimation is the process of inferring variables that are not known exactly—such as a robot’s base pose, velocity, contact state or sensor bias—from noisy measurements, control inputs and a model of how the system evolves. An estimator should provide both an estimate and, where possible, a representation of its uncertainty.
Simulation & transfer
Domain randomisation
Domain randomisation is a simulation-training technique that varies selected visual, physical or sensor parameters across generated examples or episodes. By exposing a model or policy to a distribution of environments rather than one calibrated scene, it aims to make real conditions fall within the learned variation and improve sim-to-real transfer.
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.