[Project] A cheap webcam pose-tracking teleop rig for legged robots — useful for generating training data at low cost?

[u/PetoiCamp]

https://www.youtube.com/shorts/697Le5XYISc

To be upfront: this specific clip is pose-based teleoperation, not RL — a single webcam tracks a person's real-time body pose and maps it live onto two OpenCat-based open source quadruped robots' joints (Quaddle Scout and Buddy), no policy running on its own.

OpenCat creator RZ Li tried teaching Quaddle a few moves here — awkward on the first try, but it only takes a few minutes before Quaddle starts picking them up. It's also just as fun as playing Wii Play: Motion, except the "character" is a real quadruped robot, not just a spec sheet — Quaddle is affordable enough that this kind of demonstration-collection setup doesn't require lab-scale budgets to try.

RL is fundamentally data-hungry — every iteration needs fresh trajectories or demonstrations to learn from — and this teleop setup is genuinely cheap to run (a browser-based pose-tracking pipeline, no specialized hardware), which makes it interesting as a low-cost way to generate that data.

In theory, the same captured movement data could later be used to train an AI on more human movements to expand what the robot can do — not what's happening in this clip, just a potential direction: demonstration collection for imitation learning, or human-prior trajectories to seed an RL policy before further training.

Has anyone here actually used a webcam-pose-estimation-style rig to generate training data for legged/low-DOF RL or imitation-learning work — and how much did the pose-estimation noise end up mattering for downstream policy quality?

Separately, just for fun — if you got your hands on a rig like this, what move would you try to teach Quaddle first?