An Empirical Study of Reinforcement Learning for Keypoint Positioning of Deformable Linear Objects
Overview
This research investigates reinforcement learning approaches for planar manipulation of deformable linear objects (DLOs) with one end grasped. The authors compare three Proximal Policy Optimization training stacks, finding that a GPU-parallel MuJoCo Playground setup offers faster convergence and consistent performance compared to CPU-based alternatives. By utilizing relative keypoint observations and an asymmetric actor-critic formulation, the study achieves simulation success rates of 96.4% for single-point tracking, 98.0% for disturbance recovery, and 88.8% for two-point tracking. Furthermore, policies trained with observation noise, delay, domain randomization, and disturbances successfully transfer to physical trials without fine-tuning, achieving success in 9 out of 10, 10 out of 10, and 8 out of 10 trials respectively using markerless RGB-D keypoint estimation.
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Reported quantity · Reported success rate: 96.4 percent · unique elapsed hours
“prove learning efficiency. Policies trained with observation noise, delay, domain randomization, and disturbances achieve simulation success rates of 96.4% for single-point tracking, 98.0% for disturbance recovery, and 88.8% for two-point tracking. Using markerless RGB-D keypoint estimation, the policies transfer without real-world fine-tuning, succeeding in 9/10, 10/10, and 8/10 physical trials, respectively.”
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- arXiv Robotics — research abstractsSignalAn Empirical Study of Reinforcement Learning for Keypoint Positioning of Deformable Linear Objects
This research investigates reinforcement learning approaches for planar manipulation of deformable linear objects (DLOs) with one end grasped. The authors compare three Proximal Policy Optimization training stacks, finding that a GPU-parallel MuJoCo Playground setup offers faster convergence and consistent performance compared to CPU-based alternatives. By utilizing relative keypoint observations and an asymmetric actor-critic formulation, the study achieves simulation success rates of 96.4% for single-point tracking, 98.0% for disturbance recovery, and 88.8% for two-point tracking. Furthermore, policies trained with observation noise, delay, domain randomization, and disturbances successfully transfer to physical trials without fine-tuning, achieving success in 9 out of 10, 10 out of 10, and 8 out of 10 trials respectively using markerless RGB-D keypoint estimation.
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