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About this paper
Vinay Rodrigues, Sara Zeynalpour, Gokhan Solak, Sebastian Hjorth, Arash Ajoudani
The authors train reinforcement-learning policies to position points on a one-end-grasped cable using five keypoints, then deploy them with markerless RGB-D perception. In 240 minutes, GPU-parallel MuJoCo Playground gathers approximately 13× more interactions than CPU MuJoCo stacks. Its final success rate is 0.950 ± 0.032 versus 0.880 ± 0.110 for SBX+mj. Policies transfer without fine-tuning to planar tasks on one silicone catheter.
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