From a single MuJoCo env to a full sim2real digital twin — built, benchmarked, and delivered by the team behind Silicon Valley Robotics Center.
Contact-rich pick/place, pouring, tool use, and long-horizon tabletop tasks. Franka R3, ALOHA 2, SO-100, OpenArm 101, Allegro and Shadow hands. MuJoCo, Isaac Sim, and Robosuite backends.
Flat ground, stairs, rough terrain, and pushes. Unitree G1, Go2, Booster K1, Spot. GPU-parallelized training on Isaac Lab with RSL-RL, plus MuJoCo MJX configs.
Drop-in eval harnesses for OpenVLA, Octo, RT-X, π0, Diffusion Policy, and ACT. Standardized LIBERO, CALVIN, and RLBench task suites with per-seed success rate reporting.
Calibrated dynamics, domain randomization, and a real-hardware validation pass in our Mountain View lab. Hand-off includes identified parameters and a reproducible transfer notebook.
| Robot | Isaac Sim | MuJoCo | Genesis | Gazebo |
|---|---|---|---|---|
| Unitree G1 | ✓ | ✓ | ✓ | ✓ |
| Booster K1 | ✓ | ✓ | — | ✓ |
| ALOHA 2 | ✓ | ✓ | ✓ | — |
| Franka R3 | ✓ | ✓ | ✓ | ✓ |
| Allegro Hand | ✓ | ✓ | — | — |
| Spot / Go2 | ✓ | ✓ | ✓ | ✓ |
12 tabletop tasks for a bimanual humanoid, MJX-accelerated MuJoCo + Isaac Lab parity, LIBERO-style language-conditioned eval, and per-task success curves for policy ablations. Delivered with reproducible seeds and VLA baselines.
Unitree Go2 digital twin in Isaac Lab with calibrated joint friction and actuator delay, domain-randomized rough-terrain training, and a one-shot transfer notebook that brought the trained policy to the physical robot in under 20 minutes on-site.
Drop-in Gymnasium wrappers for OpenVLA, Octo, and π0 on a custom Franka R3 tabletop suite. CI pipeline that regenerates leaderboard CSVs on every policy checkpoint, plus a static HTML report template for internal reviews.
130 tasks, lifelong learning, spatial/object/goal suites.
Long-horizonLanguage-conditioned, multi-stage tabletop manipulation.
Manipulation100+ tasks with dense reward and demo support on CoppeliaSim.
Meta-RL50-task meta-RL benchmark on Sawyer, Gymnasium API.
VLART-X / Open X-Embodiment compatible eval suite.
GeneralizationPerturbation-based generalization benchmark.
2 weeks · 1 robot · 1 simulator · 1–3 tasks. Ideal for a focused paper experiment or to validate a research idea before scaling. Gymnasium API, reward shaping, and reproducibility seeds included.
4–6 weeks · multi-robot · multi-sim · 5–10 tasks. Full benchmark-grade task suite with calibrated dynamics, domain randomization, and VLA-ready eval harnesses. Fits PhD thesis or lab-wide infrastructure.
6–8 weeks · sim2real validation. Full sim2real pipeline: calibrated dynamics, randomization, real-hardware validation in our Mountain View lab, and transfer notebook. Includes identified parameters and an on-site deployment day.
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 101. 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 Mountain View 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.