基于级联优化的接触丰富操作中同时接触选择与规划
Simultaneous Contact Selection and Planning for Contact-Rich Manipulation with Cascaded Optimization
AI总结：
提出一种级联优化框架SCSP，通过接触选择优化和接触规划优化实现接触丰富操作中的主动接触位置选择与轨迹规划。
摘要：
我们提出了一种基于优化的鲁棒接触丰富操作框架。最近的接触隐式方法能够实现跨接触模式的在线混合规划，允许针对给定的目标状态以及机器人和物体的接触位置序列进行闭环操作。然而，大多数现有方法缺乏自主推理和生成多样化接触位置序列及操作轨迹的能力，即主动接触位置选择，这限制了它们对相对简单任务的适用性。由于接触动力学中的互补性和稀疏梯度，主动接触位置选择具有挑战性，使得设计统一的接触选择与规划框架变得困难。为了解决这些挑战，我们引入了同时接触选择与规划（SCSP），这是一个级联优化框架，包括接触选择优化（CSO）和接触规划优化（CPO）。CSO利用代理接触模型和离散-连续优化来有效解决接触选择中的非光滑性和耦合问题，实现最优接触位置的在线全局搜索。CPO通过评估CSO产生的参考接触位置，并实时生成冗余机械臂对应的操作轨迹，执行先验引导的接触规划。大量的仿真和真实世界实验表明，SCSP在不准确的动力学和感知噪声下能够产生多样化的操作行为和鲁棒控制。我们进一步在具有挑战性的操作任务上验证了该框架的泛化能力。 项目网站：\href{https://sites.google.com/view/scsp-robot}{https://sites.google.com/view/scsp-robot}。
英文摘要：
We propose an optimization-based framework for robust contact-rich manipulation. Recent contact-implicit methods enable online hybrid planning across contact modes, allowing closed-loop manipulation for a given target state and contact location sequence of the robot and object. However, most existing approaches lack the ability to autonomously reason and generate diverse contact location sequences and manipulation trajectories, i.e., active contact location selection, which limits their applicability to relatively simple tasks. Active contact location selection is challenging due to complementarity in contact dynamics and the sparse gradients, making the design of a unified framework for contact selection and planning difficult. To address these challenges, we introduce Simultaneous Contact Selection and Planning (SCSP), a cascaded optimization framework comprising Contact Selection Optimization (CSO) and Contact Planning Optimization (CPO). CSO leverages a surrogate contact model and discrete-continuous optimization to efficiently resolve the nonsmoothness and coupling in contact selection, enabling online global searching of optimal contact locations. CPO performs prior-guided contact planning by evaluating the reference contact locations produced by CSO and generating corresponding manipulation trajectories in real time for redundant manipulators. Extensive simulations and real-world experiments demonstrate that SCSP produces diverse manipulation behaviors and robust control under inaccurate dynamics and perceptual noise. We further validate the generalization of the framework on challenging manipulation tasks. Project website: \href{https://sites.google.com/view/scsp-robot}{https://sites.google.com/view/scsp-robot}.
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。
