RL Environment Service

Real-world learning environments for teams that need more than simulation

Persistent, learning-ready robotic environments backed by real hardware, real sensors, and real operational support for RL, evaluation, and iteration.

Explore RL Environment Request Data Services View Data Platform
Real hardware Controlled failure Repeatable resets Benchmark-ready signals
What this means

Not a simulator. A continuously operable real-world setup.

In our context, an RL environment is a fully specified real robotic system: physical setup, clearly defined tasks and success criteria, stable observation and action spaces, deterministic reset procedures, continuous logging, and safe execution under repeated trials and failures.

This gives teams a place to train, evaluate, and iterate on learning-based policies in the real world instead of treating deployment as the first true test.

1

Define the task

Lock the task, success criteria, reset process, and observation or action interfaces.

2

Run repeated trials

Operate the same real setup across thousands of episodes with repeatable initialization.

3

Capture learning signals

Record joint states, control commands, vision, tactile or force signals, and outcomes.

4

Improve the policy

Use real failures, real edge cases, and regression tracking to iterate on the next version.

What we provide

Production-ready environment components

  • Persistent real-world environmentsDedicated setup, repeated episode execution, long-term performance tracking, and operational safety support.
  • Learning-ready signalsJoint states, control commands, proprioception, RGB and RGB-D vision, force and tactile signals, and explicit outcome labels.
  • Controlled failure at scaleSafely capture failed grasps, slips, collisions, and recovery attempts as first-class data.
Example environments

Where this gets used

  • Contact-rich manipulation - friction variability, tactile-aware insertion, slip detection, and recovery
  • Teleoperation-bootstrapped RL - human demonstrations plus online or offline RL fine-tuning
  • Regression and benchmark environments - fixed tasks, repeatable resets, and version-controlled evaluation metrics
Engagement models

Ways to work with SVRC

  • Pilot environmentShort-term setup, feasibility validation, and environment plus task co-design.
  • Persistent environmentDedicated hardware and task setup with continuous access on a monthly or quarterly basis.
  • Integrated partnershipMultiple environments, ongoing dataset growth, custom metrics, and reporting workflows.

Ready to Get Started?

Get robots, request data, or reach out — we're here to help.

Get Robot Request Data Contact Us