可推广的几何先验与递归脉冲特征学习用于双足机器人操作
Generalizable Geometric Prior and Recurrent Spiking Feature Learning for Humanoid Robot Manipulation
AI总结：
本文提出RGMP-S方法，通过几何先验和递归脉冲网络提升双足机器人操作的泛化能力与数据效率。
摘要：
双足机器人操作是执行多样化人类水平任务的关键研究领域，涉及高层语义推理和底层动作生成。然而，精确场景理解和从人类演示中高效学习样本仍是最关键的挑战，严重阻碍了现有框架的应用性和泛化能力。本文提出了一种新颖的RGMP-S，即具有脉冲特征的递归几何先验多模态策略，旨在实现高层技能推理和数据高效的运动合成。为了将高层推理 grounded 在物理现实，我们利用轻量级的2D几何归纳偏差，使视觉-语言模型能够精确理解3D场景。具体而言，我们构建了一个长周期几何先验技能选择器，有效对齐语义指令与空间约束，最终在未见过的环境中实现稳健的泛化。为了解决机器人动作生成中的数据效率问题，我们引入了递归自适应脉冲网络。我们通过递归脉冲对机器人-物体交互进行参数化，以实现时空一致性，完全蒸馏长周期动态特征，同时缓解稀疏演示场景中的过拟合问题。在Maniskill仿真基准和三个异构真实世界机器人系统上进行了广泛的实验，包括定制开发的双足机器人、桌面操作臂和商用机器人平台。实证结果证实了我们的方法优于最先进的基线，并验证了所提模块在多样化泛化场景中的有效性。为促进可重复性，源代码和视频演示已公开在https://github.com/xtli12/RGMP-S.git。
英文摘要：
Humanoid robot manipulation is a crucial research area for executing diverse human-level tasks, involving high-level semantic reasoning and low-level action generation. However, precise scene understanding and sample-efficient learning from human demonstrations remain critical challenges, severely hindering the applicability and generalizability of existing frameworks. This paper presents a novel RGMP-S, Recurrent Geometric-prior Multimodal Policy with Spiking features, facilitating both high-level skill reasoning and data-efficient motion synthesis. To ground high-level reasoning in physical reality, we leverage lightweight 2D geometric inductive biases to enable precise 3D scene understanding within the vision-language model. Specifically, we construct a Long-horizon Geometric Prior Skill Selector that effectively aligns the semantic instructions with spatial constraints, ultimately achieving robust generalization in unseen environments. For the data efficiency issue in robotic action generation, we introduce a Recursive Adaptive Spiking Network. We parameterize robot-object interactions via recursive spiking for spatiotemporal consistency, fully distilling long-horizon dynamic features while mitigating the overfitting issue in sparse demonstration scenarios. Extensive experiments are conducted across the Maniskill simulation benchmark and three heterogeneous real-world robotic systems, encompassing a custom-developed humanoid, a desktop manipulator, and a commercial robotic platform. Empirical results substantiate the superiority of our method over state-of-the-art baselines and validate the efficacy of the proposed modules in diverse generalization scenarios. To facilitate reproducibility, the source code and video demonstrations are publicly available at https://github.com/xtli12/RGMP-S.git.
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。
