用采样+局部互补，实现实时高精度抓握控制。
Approximating Global Contact-Implicit MPC via Sampling and Local Complementarity
- 分两阶段：先采样末端位置，再局部优化接触行为
- 在Franka Panda上实现非凸物体的精准非抓握操作
- 兼顾全局探索与实时性，适合复杂灵巧操作
为实现通用灵巧操作，机器人需快速规划并执行富含接触的动作。现有基于模型的控制器无法在实时内对指数级的可能接触序列进行全局优化。近期接触隐式控制虽采用简化模型，但仅做局部近似，限制了对远距离交互的利用，可能需人工干预以充分探索接触空间。本文提出新方法，结合局部互补控制优势与低维但全局的末端位置采样。核心思想是在每个控制周期中引入一个无接触阶段，先采样机器人可到达的末端位置，再在每个采样点附近预测接触丰富型MPC的成本。结果是具备全局感知的接触隐式控制器，支持实时灵巧操作。我们在Franka Panda机械臂上验证了该控制器对非凸物体的精确非抓握操作能力。
原文摘要 · Abstract (English)
To achieve general-purpose dexterous manipulation, robots must rapidly devise and execute contact-rich behaviors. Existing model-based controllers are incapable of globally optimizing in real-time over the exponential number of possible contact sequences. Instead, recent progress in contact-implicit control has leveraged simpler models that, while still hybrid, make local approximations. However, the use of local models inherently limits the controller to only exploit nearby interactions, potentially requiring intervention to richly explore the space of possible contacts. We present a novel approach which leverages the strengths of local complementarity-based control in combination with low-dimensional, but global, sampling of possible end-effector locations. Our key insight is to consider a contact-free stage preceding a contact-rich stage at every control loop. Our algorithm, in parallel, samples end effector locations to which the contact-free stage can move the robot, then considers the cost predicted by contact-rich MPC local to each sampled location. The result is a globally-informed, contact-implicit controller capable of real-time dexterous manipulation. We demonstrate our controller on precise, non-prehensile manipulation of non-convex objects using a Franka Panda arm. Project page: https://approximating-global-ci-mpc.github.io
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