Principle:ARISE Initiative Robosuite Observable System
| Knowledge Sources | |
|---|---|
| Domains | Robotics, Sensor Modeling, Software Architecture |
| Last Updated | 2026-02-15 07:00 GMT |
Overview
A sensor abstraction system that defines observable quantities with configurable sampling rates, noise corruption, temporal delay, and filtering, enabling realistic sensor modeling and modular observation composition in simulation environments.
Description
Real robotic sensors do not provide perfect readings: they have limited sampling rates, measurement noise, temporal delays, and filtering characteristics. A simulation framework that aims for realistic sensor modeling needs an abstraction layer that can apply these effects to any simulated measurement. The observable system provides this layer by wrapping raw sensor functions in Observable objects that manage sampling, corruption, delay, and caching.
At the foundation, a sensor is defined as a decorated Python function that reads some quantity from the simulation state. The decorator marks the function with a modality tag (such as "proprio" for proprioceptive or "object" for object state) that categorizes the type of sensory data. The Observable wrapper then adds temporal behavior: the sensor is sampled at a configurable rate (which may be lower than the simulation rate), readings can be corrupted by deterministic offsets, uniform noise, or Gaussian noise, and a delay buffer can hold readings for a specified number of timesteps before making them available.
The system supports an observation cache that allows observables to share pre-computed values. When one observable's sensor function computes an intermediate result (such as a joint position array), other observables (such as joint position cosine or sine) can read from the cache instead of redundantly querying the simulation. Observables can be individually activated or deactivated, allowing environments to control which observations are included in the observation dictionary without modifying sensor definitions.
Usage
Apply this principle when defining the observation space of a simulation environment. Each measurement that the robot can access -- joint positions, end-effector pose, camera images, force-torque readings -- should be wrapped in an Observable with appropriate sampling rate and noise parameters. This is especially important for sim-to-real transfer, where matching the real sensor characteristics in simulation reduces the reality gap.
Theoretical Basis
The observable system uses a decorator + wrapper architecture:
@sensor(modality="proprio")
def joint_pos(obs_cache):
return sim.data.qpos[joint_indexes]
obs = Observable(
name="robot0_joint_pos",
sensor=joint_pos,
sampling_rate=20, # Hz
active=True,
)
Sensor corruption applies measurement noise:
Deterministic: corrupted = clip(value + offset, low, high)
Uniform noise: corrupted = clip(value + U(min_noise, max_noise), low, high)
Gaussian noise: corrupted = clip(value + N(0, sigma), low, high)
Temporal delay buffers readings:
At each sample step:
buffer.push(current_reading)
observable_value = buffer[t - delay]
Observation cache enables value sharing between sensors:
@sensor(modality="proprio")
def joint_pos(obs_cache):
return sim.data.qpos[indexes]
@sensor(modality="proprio")
def joint_pos_cos(obs_cache):
return np.cos(obs_cache["robot0_joint_pos"])
@sensor(modality="proprio")
def joint_pos_sin(obs_cache):
return np.sin(obs_cache["robot0_joint_pos"])
The joint_pos value is computed once and reused by both cosine and sine sensors.
Sampling rate control subsamples the simulation rate:
if current_sim_step % (sim_rate / sampling_rate) == 0:
value = sensor(obs_cache)
apply corruption and delay
else:
value = previous_value (hold last sample)