Helios — 6-DOF Pick-and-Place Controller
A reinforcement learning policy for a UR5e collaborative robot arm, trained end-to-end in IsaacGym and transferred to hardware with minimal fine-tuning. The core contribution is a domain randomisation schedule that gradually narrows the simulation-reality gap over training, combined with a learned force/torque residual that compensates for unmodelled contact dynamics. Achieves 94% grasp success rate across 30+ unseen object geometries without any geometry-specific calibration.