发表机构
Cornell University; Carnegie Mellon University; Toyota Research Institute（康奈尔大学; 卡内基梅隆大学; 丰田研究机构）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究全臂操作问题，提出TACTIC控制器，它采用以接触为中心的混合预测模型，结合多种传感，通过接触雅可比矩阵耦合动力学与运动学，集成到MPC规划器，在模拟和实际任务中表现出色，优于其他方法。
AI 中文摘要
全臂操作涉及与环境直接接触，机器人通过在接触形成、滑动和断裂时将接触分布在多个连杆上来完成任务。这种设置打破了许多基于学习的操作流程中的常见隐含假设。为解决此问题，我们提出了TACTIC（触觉和视觉条件下的以接触为中心的控制），一种用于全臂操作的滚动时域控制器。TACTIC使用以接触为中心的混合预测模型，结合RGB-D、分布式触觉传感和紧凑的二维接近度表示。该模型通过接触雅可比矩阵将学习到的、动作条件化的潜在动力学模型与解析运动学相结合。TACTIC将这些展开集成到基于采样的MPC规划器中，采用接触感知动作采样。我们在模拟中评估了TACTIC，并进行了消融实验以分离每个设计选择的贡献。TACTIC始终优于其他方法。我们还在具有分布式触觉传感的机器人上展示了其在三个需要多接触轨迹的全臂操作任务中的实际性能。
英文摘要
Whole-arm manipulation involves direct contact with the environment while the robot completes a task by distributing contact across multiple links as contacts form, slide, and break. This setting breaks common implicit assumptions in many learning-based manipulation pipelines: arm configuration tightly couples motion and contact forces, contact state is partially observed under occlusion, and purely learned rollouts can become physically inconsistent under distribution shift because many multi-link contact configurations are sparsely represented in the data. To address this, we propose TACTIC (Tactile and Vision Conditioned Contact-Centric Control), a receding-horizon controller for whole-arm manipulation. TACTIC uses a contact-centric hybrid predictive model that combines RGB-D, distributed tactile sensing, and a compact 2D proximity representation. The model couples a learned, action-conditioned latent dynamics model with analytical kinematics through contact Jacobians, enabling rollouts of future contact configurations and interaction forces. TACTIC integrates these rollouts into a sampling-based MPC planner with contact-aware action sampling: contact Jacobian-based projections steer sampled action sequences toward force-modulating directions, and objectives defined over predicted proximity and interaction forces trade task progress against whole-arm force regulation. We evaluate TACTIC in simulation against state-of-the-art model-based and model-free methods, and perform ablations that isolate the contribution of each design choice. TACTIC consistently outperforms other methods. We further demonstrate real-world performance on a robot with distributed tactile sensing across three whole-arm manipulation tasks that require multi-contact trajectories: turning over and repositioning a manikin, and goal-reaching in a 3D dynamic maze. Website: https://emprise.cs.cornell.edu/tactic
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