CATCH-FORM-ACTer：基于合规感知触觉控制与混合形变调节的粘弹性物体操作动作Transformer
CATCH-FORM-ACTer: Compliance-Aware Tactile Control and Hybrid Deformation Regulation-Based Action Transformer for Viscoelastic Object Manipulation
AI总结：
针对刚性机器人自动化粘弹性物体富接触操作的挑战，提出CATCH-FORM-ACTer，结合ACT框架增强CATCH-FORM-3D，通过LfD构建长时程序列并实时调整参数，实验成功率较传统方法高10%-20%。
摘要：
使用刚性机器人自动化粘弹性物体的富接触操作面临动态参数不匹配、不稳定接触振荡及时空力-形变耦合等挑战。在前期工作中，合规感知触觉控制与混合形变调节（CATCH-FORM-3D）策略实现了3D粘弹性物体的鲁棒有效操作，结合接触力驱动的导纳外环与PDE稳定内环，达到亚毫米级表面形变精度。但该策略需微调物体特定参数和任务特定校准，为此提出CATCH-FORM-ACTer，通过动作分块Transformer（ACT）框架增强CATCH-FORM-3D。直观遥操作系统执行示范学习（LfD）构建长时程感知、决策与执行序列；与仅关注轨迹规划的传统ACT方法不同，该方法在多阶段操作中实时动态调整刚度、阻尼和扩散参数，有效模仿类人力-形变调制。单臂/双臂机器人在三项任务中的实验显示，其力场模式更优，成功率较传统方法高10%-20%，可实现工业、医疗或家庭场景的精确安全交互。
英文摘要：
Automating contact-rich manipulation of viscoelastic objects with rigid robots faces challenges including dynamic parameter mismatches, unstable contact oscillations, and spatiotemporal force-deformation coupling. In our prior work, a Compliance-Aware Tactile Control and Hybrid Deformation Regulation (CATCH-FORM-3D) strategy fulfills robust and effective manipulations of 3D viscoelastic objects, which combines a contact force-driven admittance outer loop and a PDE-stabilized inner loop, achieving sub-millimeter surface deformation accuracy. However, this strategy requires fine-tuning of object-specific parameters and task-specific calibrations, to bridge this gap, a CATCH-FORM-ACTer is proposed, by enhancing CATCH-FORM-3D with a framework of Action Chunking with Transformer (ACT). An intuitive teleoperation system performs Learning from Demonstration (LfD) to build up a long-horizon sensing, decision-making and execution sequences. Unlike conventional ACT methods focused solely on trajectory planning, our approach dynamically adjusts stiffness, damping, and diffusion parameters in real time during multi-phase manipulations, effectively imitating human-like force-deformation modulation. Experiments on single arm/bimanual robots in three tasks show better force fields patterns and thus 10%-20% higher success rates versus conventional methods, enabling precise, safe interactions for industrial, medical or household scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
