An ACT policy trained on the flatten_tshirt task of a bimanual deformable-object (cloth / bag) manipulation benchmark. The robot is a dual-arm Piper; the task is to flatten a crumpled t-shirt on a table.

Simulation uses a GPU cloth solver co-simulated with the robot in a single model, and observations are rendered with a photorealistic renderer.

Augmentation follows a tuned recipe (brightness / contrast / saturation / hue / sharpness / small affine) rather than the LeRobot default, which is disabled. The brightness range is deliberately asymmetric toward the darker side: simulation lighting is idealized while real RealSense D435i footage tends to be darker, so biasing the augmentation toward darker samples is the right direction for sim-to-real transfer.

⚠️ This checkpoint has not yet been formally evaluated. It has only been through a small smoke-level closed-loop run, not the benchmark's standard N=100 protocol. Success-rate numbers are deliberately not published here yet; they will be added once the full evaluation has been run. Treat this as a training artifact, not a reported result.

The policy expects the three camera streams named exactly as listed above, plus a 14-D observation.state, and returns a 14-D action. It runs in-process (no policy server needed).

Apache-2.0.