Teleoperation at Scale — Teaching Robots in Simulation

If data is the foundation of robotics intelligence, simulation is its accelerator. Teleoperation—humans controlling robots inside simulation—offers a scalable way to teach robots fine-grained movements without expensive hardware wear and tear.

Why Teleoperation?

-Robots in real life are costly and fragile.

-Simulation platforms (NVIDIA Isaac Sim, Mujoco, Unity) let us replicate physics with near-realistic fidelity.

-Humans can control robots with haptic gloves, controllers, or VR rigs, generating expert trajectories at scale.

Advantages

-Speed: thousands of robots can be simulated in parallel.

-Safety: training on risky motions (surgery, firefighting) without real-world accidents.

-Cost: avoids the high expense of breaking hardware during exploration.

Limits Today

-Fine-grained dexterity (sewing, threading needles, playing piano) remains challenging: physics engines approximate but don’t perfectly capture micro-frictions, elastic deformations, or tactile feedback.

-Latency: current teleop rigs introduce slight delays; acceptable for gross motion, problematic for high-frequency manipulation.

Human Wave’s Vision

-Massive contributor base performing teleoperation inside simulation environments.

-Data fused with real-world mocap to bridge the sim-to-real gap.

-Dedicated research on tactile-informed teleop (gloves with pressure sensors).

Just as reinforcement learning leapt forward with Atari and MuJoCo simulations, robotics will leap with teleoperation at scale. The next generation of robots will be trained in parallel universes before touching the real one.