Grip, Trace, Write: Force-Aware Imitation and Reinforcement Learning for High-DoF Robotic Handwriting
Grip, Trace, Write: Force-Aware Imitation and Reinforcement Learning for High-DoF Robotic Handwriting
This paper presents Grip, Trace, Write, a force-aware simulation environment and learning framework for high-DoF robotic handwriting. The approach uses a two-branch actor to separate pen retention from arm-wrist writing while preserving their closed-loop coupling. Training involves learning a grasp controller, initializing writing via inverse-kinematics demonstrations using behavior cloning, and improving performance through reinforcement learning with rewards on ink, force, time, and retention. Results show improved completion, precision, force regulation, and efficiency over baseline methods, with generalization to unseen English and Chinese content without fine-tuning.
The authors introduce a simulation environment and learning framework that couples grasp stability, tip motion, and contact force for high-DoF robotic handwriting, demonstrating improvements in completion, precision, force regulation, and efficiency over behavior cloning and inverse kinematics across diverse tasks without fine-tuning to unseen content.
Source: arXiv Robotics — research abstracts · Read original article ↗
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arXiv:2611.10451v1 Announce Type: new Abstract: Most robotic handwriting rigidly mounts the pen and reduces writing to trajectory tracking. A freely held pen instead couples grasp stability, tip motion, and contact force. We present Grip, Trace, Write, a force-aware simulation environment and learning framework for high-DoF handwriting. A two-branch actor separates pen retention from arm-wrist writing while preservin
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Source：arXiv Robotics — research abstracts · arxiv.org