外观
论文arXiv 2607.157013 个月前
RAVEN：用于鲁棒人形导航的强化自适应可见图规划与无碰撞 MPC
原文链接：RAVEN: Reinforcement-Adaptive Visibility-Graph Planning for Robust Humanoid Navigation with Collision-Free MPC（arXiv 2607.15701 · 发布日期：2026-07-17）
RAVEN: Reinforcement-Adaptive Visibility-Graph Planning for Robust Humanoid Navigation with Collision-Free MPC
RAVEN用强化学习自适应可见图几何参数，并结合无碰撞MPC，在延迟和噪声下验证鲁棒人形导航。
核心要点
- 人形导航需长时域规划，同时满足短时域动态与安全约束。
- RAVEN用RL调整障碍物膨胀等可见图参数，重塑自由空间几何。
- 无碰撞MPC跟踪规划轨迹，并显式施加速度边界和避障约束。
- 相较手调可见图MPC和纯RL策略，结果显示过冲更小、鲁棒性更好。
验证范围与局限：验证范围包括延迟、观测噪声和狭窄通道下的人形导航；摘要未说明仿真或真机。
英文摘要 (Abstract)
Humanoid navigation in dynamic environments requires long-horizon planning while respecting short-horizon dynamic and safety constraints. Classical visibility-graph planners combined with model predictive control (MPC) can efficiently generate collision-free trajectories, but their performance depends on manually tuned parameters and accurate system modeling. In real robotic systems, control delays, state-estimation noise, and locomotion uncertainties can cause overshoot and constraint violations even when the nominal path is geometrically optimal. We propose RAVEN, a hierarchical reinforcement learning (RL)-MPC framework for robust humanoid navigation. Unlike prior approaches that use learning to tune cost weights or replace planning entirely, RAVEN employs RL to adapt the geometric construction of a visibility-graph planner by modifying obstacle inflation and related graph parameters. By directly reshaping the free-space geometry, the learned planner alters the topology of the global path to compensate for delay and tracking imperfections. A collision-free MPC layer then tracks the planned trajectory while explicitly enforcing velocity bounds and obstacle-avoidance constraints. By training under realistic delays and observation noise, RAVEN learns planning adaptations that improve robustness while retaining explicit long-horizon geometric planning and constrained optimization, in contrast to end-to-end learning approaches. We evaluate RAVEN against a manually tuned visibility-graph MPC baseline and a pure RL navigation policy. Results demonstrate reduced overshoot near obstacles, improved robustness in narrow passages, and more reliable navigation under delay and noise. These findings indicate that reinforcement-adaptive graph construction combined with constrained MPC provides an effective and interpretable alternative to end-to-end learning for robust humanoid navigation.
作者：Hou, Ruochen, Shiqi Wang, Beom Jun Kim, Hanzhang Fang, Mehak Singal, Dennis W. Hong
来源
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