Lightwheel

Today, Lightwheel is introducing RoboFinals RL-Env.

RL environments in simulation, built from real-world tasks and calibrated physics.

Most RL environments in simulation get two things wrong. The task is invented, not taken from real work. And the physics is tuned until it looks right, not calibrated.

RoboFinals RL-Env fixes both. Each task starts as a person doing the real work, captured by Lightwheel EgoSuite, and ends as an environment you can train in.

The first task is automotive repair. What’s inside:

→ Real tasks, captured by EgoSuite
→ Long-horizon, multi-stage tasks
→ Deformable, physically calibrated
→ Dexterous, contact-rich interaction

It runs Newton-native. And it shares its task definition with RoboFinals, so a policy trained here is evaluated on the same terms it was trained on.

One more thing. As EgoSuite grows, agents build new environments to match what it sees, and our experts review each one. One pipeline, every task.

Early access is open. If you train manipulation policies, join the waitlist. If you own a real task — a line, a ward, a warehouse — become a RoboFinals partner, and let’s make it a benchmark task.

lightwheel.ai/robofinals-rl-…

#PhysicalAI #ReinforcementLearning #Robotics #Simulation #RLEnv

@LightwheelAI x @UnitreeRobotics: partnering to advance real-world deployment.

Our shared goal: robots that perform reliably in the real world, improve with every deployment, and scale to industrial use.

The partnership brings together Lightwheel's physical AI infrastructure for data, simulation, evaluation, and deployment with Unitree's expertise in robot hardware, motion control, perception, and dexterous manipulation. Three areas of focus:

• Data: Egocentric human data from EgoSuite, built around the real-world tasks Unitree's robots are designed to perform.
• Simulation: Reinforcement learning in simulation, with industrial-grade evaluation on RoboFinals before robots enter service.
• Deployment: Through RoboStack, operational data from Unitree robots in the field returns to simulation and training, so each deployment strengthens the next.

Industrial-scale deployment takes hardware, models, data, and operations working as one system. Together, Lightwheel and Unitree are building that system, and the foundation for robots that keep improving throughout their working lives.

#Lightwheel #Unitree #PhysicalAI #Robotics

Join Christian Ureno at #CoRL2026 today on how Lightwheel scales robot learning with data and simulation.

📅 1:40 PM CT · 📍 Hilton Austin

Robots learn from humans and in simulation. Both have to be real. 100K hrs of human data w/ @huggingface, physics accurate enough for RL and RoboFinals eval, real robots feeding it all back.

Working on Physical AI? Come find us.

We’re co-hosting this at @corl_conf. Small table, strong opinions on sim-to-real, and the people actually deploying robots. See you in Austin 🍸

Lightwheel brings physical accuracy to shared simulation assets. Now live on @NVIDIA SimReady Central.

Most simulation assets look right. Fewer behave right. Our work on SimReady Central is on the side that is harder to see: whether an asset’s contact, friction, mass, and joints hold up when a robot interacts with it in simulation.

What we do:
→ Agentic QA: simulate, tune collision and articulation, test again
→ Physical tuning grounded in real-world measurement
→ Schema feedback so physical properties are described consistently across solvers

If the physics is wrong, the benchmark is too.

Thanks @NVIDIARobotics @nvidiaomniverse for building SimReady Central in the open.