CADeT：用于软组织间接机器人操作中的因果感知变形传递
CADeT: Causal-Aware Deformation Transmission for Indirect Robotic Manipulation of Soft Tissue
- University of Leeds（利兹大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对机器人辅助微创手术中软组织间接操作的模糊性问题，提出因果感知变形传递框架CADeT，结合结构因果模型与主动感知，实现模式识别与形状控制，验证中优于基线。
AI中文摘要：
在机器人辅助微创手术（RAMIS）中，对机器人无法触及的深层可变形解剖结构进行间接操作具有挑战性，因为中间组织会在空间上过滤变形传递。被动观察可能具有模糊性，因为解耦模式和阻塞模式可能产生相似的运动响应。我们提出了CADeT，一个因果感知变形传递框架，该框架将结构因果模型（SCM）与主动感知相结合，以推断潜在传递模式并在线估计状态依赖的粘附雅可比矩阵。在正常操作期间，控制动作更新模式信念；当模糊性持续存在时，选择额外的探测动作以提高模式可区分性。模式信念和学习到的雅可比矩阵被纳入信念感知模型预测控制器中，用于间接目标形状控制。在仿真和达芬奇研究套件（dVRK）上，使用体模和离体猪组织进行的验证表明，与评估的无模型和基于模型的基线相比，模式识别准确率更高，形状误差收敛更快。这些结果表明，在所评估的条件下，主动感知改善了模式识别和间接变形控制。
英文摘要：
Indirect manipulation of deep-seated deformable anatomy inaccessible to the robot is challenging in robot-assisted minimally invasive surgery (RAMIS) because intervening tissues spatially filter deformation transmission. Passive observations can be ambiguous because the Decoupled and Blocked modes may produce similar motion responses. We propose CADeT, a causal-aware deformation transmission framework that integrates structural causal model (SCM) with active sensing to infer a latent transmission mode and estimate a state-dependent adhesion Jacobian online. During normal manipulation, control actions update the mode belief; when ambiguity persists, an additional probing action is selected to improve mode distinguishability. The mode belief and learned Jacobian are incorporated into a belief-aware model predictive controller for indirect target-shape control. Validation in simulation and on the da Vinci research kit (dVRK), using phantom and ex vivo porcine tissues, shows higher mode-identification accuracy and faster shape-error convergence than the evaluated model-free and model-based baselines. These results show that active sensing improves mode identification and indirect deformation control under the evaluated conditions.
