Custom Reinforcement Learning Environments for Robotics Research

A reinforcement learning environment is a simulator or physical rig that provides an agent (a policy or controller) with observations, accepts actions, and returns rewards and next-state observations. For robotics, RL environments typically wrap a physics engine (MuJoCo, PhysX, Bullet) with a standard API such as Gymnasium, and include robot URDFs/MJCFs, scenes, reward functions, and reset logic.

Use MuJoCo when you need fast contact-rich manipulation iteration, CPU-friendly experiments, or minimal setup. Use Isaac Sim when you need photorealistic rendering, GPU-accelerated parallel rollouts for humanoid locomotion, or USD-based scene composition at scale. See our detailed MuJoCo vs Isaac Sim 2026 guide.

A Starter environment (single task, single robot, one simulator) typically ships in 2 weeks. A Suite (5–10 tasks, multiple robots, calibrated dynamics) takes 4–6 weeks. A full sim2real Digital Twin with domain randomization and real-hardware validation takes 6–8 weeks.

We ship calibrated URDFs/MJCFs for Unitree G1, Unitree Go2, Booster K1, ALOHA 2, Franka Research 3, Allegro Hand, Shadow Hand, Boston Dynamics Spot, SO-100, and OpenArm 1. Adding a new robot typically costs $2–5K depending on URDF quality and whether we need to identify dynamics parameters from hardware.

Yes. Our Digital Twin tier includes domain randomization across friction, mass, sensor noise, and actuator delay, plus a validation pass on the physical robot in our San Francisco lab. We hand back the identified dynamics parameters and a reproducible policy-transfer notebook.

Yes. Our VLA Evaluation environments wrap the policy-under-test in a standard Gymnasium action space and stream RGB observations at inference-appropriate rates. We provide evaluation scripts for OpenVLA, Octo, RT-X, Diffusion Policy, ACT, and π0, and can add new policies on request.

Anything that speaks Gymnasium: Stable-Baselines3, CleanRL, RLlib, Tianshou, as well as RL-Games and RSL-RL for Isaac Lab. We can also deliver IsaacLab-native RL-Games configs, SKRL configs, or Robosuite Robomimic imitation-learning configs.

Every shipped environment includes a matching benchmark task set chosen from LIBERO, CALVIN, RLBench, Meta-World, ManiSkill, Humanoid-Bench, or a custom task suite. Results are reported with per-task success rates, seeds, and reproducibility instructions.

Default delivery is a private Git repo licensed to your team with perpetual internal use. We offer an open-source add-on (MIT or Apache-2.0) at no extra cost when the upstream components allow it. Commercial redistribution rights are negotiable.

Fill out the contact form below with your robot, task, preferred simulator, and deadline, or email contact@roboticscenter.ai. Most quotes come back within one business day.