RL Environments as a Service

Real-world reinforcement learning environments for production robotics teams

We provide persistent, learning-ready robotic environments backed by real hardware, real sensors, and real operational support — enabling robotics teams to train, evaluate, and iterate on RL and learning-based policies in the real world, not just in simulation.

This service is designed for applied robotics teams moving beyond prototypes, where simulation alone no longer captures the failure modes, contact dynamics, and edge cases that matter in production.

What We Mean by “Environment”

We do not offer simulators.

An RL environment, in our context, is a fully specified, continuously operable system, including:

  • A physical robotic setup (arm, end-effector, sensors)

  • Clearly defined tasks and success criteria

  • Stable observation and action spaces

  • Deterministic reset and initialization procedures

  • Continuous data logging and evaluation signals

  • Safe execution under repeated trials and failures

In other words, we provide the thing your policy actually trains against.

What We Provide

Persistent Real-World Environments

Each environment is designed to run day after day, supporting:

  • Thousands of episodes

  • Online or offline RL

  • Regression testing across policy versions

  • Long-term performance tracking

We handle hardware setup, calibration, maintenance, and operational safety, so your team can focus on learning and control.

Learning-Ready Signals

Environments expose production-relevant signals, including:

  • Joint states, control commands, and proprioception

  • Vision (RGB / RGB-D, multi-view if needed)

  • Force and tactile feedback for contact-rich tasks

  • Explicit success, failure, and termination conditions

All signals are time-synchronized and structured to plug directly into training and evaluation pipelines.

Controlled Failure at Scale

Real robots fail — and production RL systems must learn from it.

Our environments are designed to:

  • Safely execute failed grasps, slips, collisions, and recovery attempts

  • Capture failure trajectories as first-class data

  • Surface edge cases that simulators consistently miss

This enables more robust policies and faster iteration cycles.

Example Production Environments

Contact-Rich Manipulation

  • Grasping and repositioning under friction variability

  • Tactile-aware insertion and alignment

  • Slip detection and recovery

Why teams use it:

Policies trained purely in simulation often overfit ideal contact. Real tactile and force feedback exposes failure modes early.

Teleoperation-Bootstrapped RL

  • Human-in-the-loop demonstrations to initialize policies

  • Online or offline RL fine-tuning

  • Continuous dataset expansion during deployment

Why teams use it:

Faster convergence and safer early-stage learning on real hardware.

Regression & Benchmark Environments

  • Fixed task definitions

  • Repeatable resets

  • Version-controlled evaluation metrics

Why teams use it:

To ensure policy updates don’t silently regress real-world performance.

How Teams Use RL-EaaS

Applied robotics teams typically use our environments to:

  • Train policies that already “work in sim” but fail in reality

  • Validate sim-to-real transfer before deployment

  • Collect real-world data for offline RL

  • Benchmark competing policies under identical conditions

  • Run long-horizon tests without building internal infra

Engagement Models

We support multiple engagement modes, depending on where your team is:

Pilot Environment

  • Short-term setup

  • Feasibility validation

  • Environment and task co-design

Persistent Environment

  • Dedicated hardware and task setup

  • Continuous access for training and evaluation

  • Monthly or quarterly engagement

Integrated Partnership

  • Multiple environments

  • Ongoing dataset growth

  • Custom metrics and reporting

  • Long-term collaboration

All engagements can be structured under NDA and aligned with internal security and data governance requirements.

Why Not Just Simulation?

Simulation is essential — but incomplete.

Teams come to us when they encounter:

  • Contact dynamics that don’t transfer

  • Grasp stability issues invisible in sim

  • Policies that pass benchmarks but fail in deployment

  • Hardware-specific edge cases

Our environments exist where simulation stops being predictive.

Why Silicon Valley Robotics Center

We are not a generic data vendor or testing lab.

We operate at the intersection of:

  • Real robotic hardware

  • Learning-based control

  • Production-oriented data pipelines

Our environments are built by teams who understand both RL algorithms and physical systems, and who know what breaks when models meet reality.

Get Started

If you are building production robotics systems and need real-world RL environments you can rely on, we’re happy to discuss your task, constraints, and deployment timeline.

Contact us to explore a pilot or long-term environment engagement.