Principle:ARISE Initiative Robosuite Sensor Corruption Modeling
| Knowledge Sources | |
|---|---|
| Domains | Robotics, Sim_to_Real_Transfer |
| Last Updated | 2026-02-15 07:00 GMT |
Overview
A framework for injecting realistic sensor imperfections -- including additive noise, measurement delay, and configurable sampling rates -- into simulation observations to train policies that are robust to real-world sensor degradation.
Description
Simulated sensors produce perfect, instantaneous readings that do not reflect the imperfections of physical hardware. Real sensors suffer from measurement noise (electronic interference, quantization, thermal drift), latency (processing time, communication delays), and limited sampling rates. Policies trained exclusively on clean simulation data often fail when deployed on real robots because they have never experienced these imperfections. Sensor corruption modeling bridges this sim-to-real gap by systematically degrading simulation observations to match the characteristics of physical sensors.
The corruption framework operates through the Observable abstraction, which wraps each sensor reading with a configurable processing pipeline. Each observable has four configurable stages: a sensor function that produces raw data, a corrupter that adds noise, a filter that post-processes the signal, and a delayer that introduces temporal lag. The observable also has a configurable sampling rate that determines how frequently new measurements are taken, independent of the simulation timestep.
Corrupters implement different noise models. Gaussian noise corrupters add zero-mean noise with a specified standard deviation, suitable for modeling electronic noise in force-torque sensors and encoders. Uniform noise corrupters add noise sampled from a uniform distribution, suitable for quantization-like effects. Deterministic corrupters add a fixed bias, suitable for modeling calibration offsets. All corrupters support output clamping to ensure values remain within physically meaningful ranges.
Delayers model measurement latency. Deterministic delayers introduce a fixed delay, while sampled delayers draw delay values from uniform or Gaussian distributions at each sampling event. The delay interacts with the sampling rate: a measurement is taken at the time t_sample - delay, meaning the observable reports data from a point in the past. This accurately models the pipeline latency of real sensor systems.
Different sensor modalities receive different corruption profiles. Camera images are corrupted with pixel-level Gaussian noise and delayed to simulate camera processing latency. Proprioceptive signals (joint positions) receive noise scaled proportionally to the joint range, with shorter delays reflecting their faster electronic readout.
Usage
Use sensor corruption when training policies intended for real-robot deployment. The corruption parameters should be calibrated to match the known noise characteristics of the target hardware. Corruption can also be toggled dynamically during training to implement curriculum learning, starting with clean observations and gradually increasing corruption as the policy improves. During evaluation, corruption can be disabled to measure the policy's baseline performance, or applied at varying levels to assess robustness.
Theoretical Basis
Observable processing pipeline:
raw_value = sensor(obs_cache)
corrupted_value = corrupter(raw_value)
filtered_value = filter(corrupted_value)
observed_value = filtered_value (recorded at time t - delay)
Gaussian noise model:
corrupter(x) = clip(x + N(mu, sigma^2), low, high)
where N(mu, sigma^2) is a Gaussian random variable with mean mu and variance sigma^2, and clip enforces output bounds.
Uniform noise model:
corrupter(x) = clip(x + U(min_noise, max_noise), low, high)
where U(a, b) is a uniform random variable on [a, b].
Delay model:
delay = U(min_delay, max_delay) (uniform sampled)
delay = max(0, N(mean_delay, std)) (Gaussian sampled)
delay = constant (deterministic)
Sampling rate interaction:
sampling_period = 1 / sampling_rate
time_since_last_sample += timestep
if not sampled and (sampling_period - delay >= time_since_last_sample):
take_measurement()
sampled = True
delay = delayer() # resample delay for next period
if time_since_last_sample >= sampling_period:
time_since_last_sample = time_since_last_sample mod sampling_period
sampled = False
This model ensures that measurements are taken at the appropriate delayed time within each sampling period, closely approximating the behavior of real sensor systems with finite bandwidth and processing latency.