提出IMPACT方法，让机器人在复杂接触任务中更快更稳地规划动作。
IMPACT: An Implicit Active-Set Augmented Lagrangian for Fast Contact-Implicit Trajectory Optimization
- 基于隐式激活集的增广拉格朗日法，实时识别接触模式分支。
- 相比强基线速度提升2.9至70倍（几何均值13.8倍）。
- 适用于高难度操作与真实硬件，尤其适合接触频繁的机器人任务。
接触隐式轨迹优化（CITO）作为接触密集型机器人任务中统一规划与控制的框架，近年来受到广泛关注。现有方法在无需预设接触模式的情况下，已在操作与运动任务中取得良好效果。然而，其背后的数学规划问题（含互补约束，MPCC）仍存在数值病态问题，系统性且可扩展的求解策略仍是研究热点。因此，高效且严谨的求解器对拓展CITO应用至关重要。本文提出一种增广拉格朗日方法用于求解基于MPCC的CITO，具备最优性保证。该方法可在轨迹优化迭代过程中动态识别隐式接触模式分支，命名为IMPACT（IMPlicit contact ACtive-set Trajectory optimization）。我们实现了针对轨迹优化负载的高效C++版本，并在开源的CITO与接触隐式模型预测控制（CI-MPC）基准上进行评估。在CITO任务中，相比强基线实现2.9x–70x加速（几何均值13.8x）；在CI-MPC中，提升了复杂接触轨迹的控制质量。最后，我们在真实机器人硬件上验证了该方法在T形物体推移任务中的有效性。
原文摘要 · Abstract (English)
Contact-implicit trajectory optimization (CITO) has attracted growing attention as a unified framework for planning and control in contact-rich robotic tasks. Recent approaches have demonstrated promising results in manipulation and locomotion without requiring a prescribed contact-mode schedule. It is well known that the underlying mathematical programs with complementarity constraints (MPCCs) remain numerically ill-conditioned, and systematic, scalable solution strategies for CITO remain an active area of research. More efficient and principled solvers that can handle contact constraints are therefore essential to broaden the applicability of CITO. In this work, we develop an augmented-Lagrangian approach to CITO for solving MPCC-based CITO with stationarity guarantees. The method can be interpreted as identifying the implicit contact-mode branches on the fly during the trajectory optimization (TO) iterations; we call this approach IMPACT (IMPlicit contact ACtive-set Trajectory optimization). We provide an efficient C++ implementation tailored to trajectory-optimization workloads and evaluate it on the open-source CITO and contact-implicit model predictive control (CI-MPC) benchmarks. On CITO, IMPACT achieves 2.9x-70x speedups over strong baselines (geometric mean 13.8x). On CI-MPC, we show improved control quality for contact-rich trajectories on dexterous manipulation tasks in simulation. Finally, we demonstrate the proposed method on real robotic hardware on a T-shaped object pushing task.
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