外观
论文arXiv 2508.098462025-08-13
轮式人形遥操作框架集成多阶段物体参数估计实现动态搬运
本文由 AI 根据公开内容整理，可能有误，请以原文为准：Whole-Body Bilateral Teleoperation with Multi-Stage Object Parameter Estimation for Wheeled Humanoid Locomanipulation（arXiv 2508.09846）
核心内容
针对轮式人形机器人loco-manipulation任务，研究团队提出一种物体感知全身双边遥操作框架。核心创新在于集成在线多阶段物体惯性参数估计模块，通过视觉尺寸估计器、大视觉语言模型（VLM）生成初始猜测及解耦分层采样策略，实现对未知负载质量、质心及惯量的实时辨识。该系统已在搭载机械臂与人机接口的自定义轮式人形平台上完成验证，成功执行举升、运送及释放任务，有效负载达机器人自重的三分之一。
技术方案采用分层估计策略：先估质量与质心，再基于尺寸推断惯量以确保物理可行性，并利用解耦多假设机制增强对VLM先验误差的鲁棒性。估计器与高保真仿真及硬件并行运行，实时更新轮式人形平衡点，使操作员聚焦于运动与操作本身。通过补偿物体动力学，该框架在保持柔顺行为的同时改善了触觉力反馈的动态同步性，显著提升了复杂动态场景下的全身遥操作性能与操纵跟踪精度。
关键看点
- 多阶段参数估计：融合视觉尺寸与VLM先验生成初始猜测，结合解耦分层采样策略，大幅缩减搜索空间并提升在线辨识速度
- 动态负载验证：在自定义轮式人形机器人上实测，支持实时完成举升、交付与释放任务，有效负载重量约为机器人本体自重的1/3
- 力反馈增强：利用估计参数实时更新平衡点并补偿物体动力学，改善触觉力反馈同步性，同时保持柔顺控制与操纵跟踪精度
- 鲁棒性设计：采用解耦多假设方案应对VLM先验误差，确保在视觉或语义信息偏差下仍能输出物理可行的惯性参数
工程与产业观察
该工作解决了具身智能遥操作中未知物体动力学适配难题，通过VLM与物理采样融合实现毫秒级参数辨识。其将语义先验转化为控制层可用的物理约束，为轮式人形在非结构化环境中的动态重物搬运提供了可复现的工程范式，对提升人机协作效率具有直接参考价值。
英文摘要 (Abstract)
This paper presents an object-aware whole-body bilateral teleoperation framework for wheeled humanoid loco-manipulation. This framework combines whole-body bilateral teleoperation with an online multi-stage object inertial parameter estimation module, which is the core technical contribution of this work. The multi-stage process sequentially integrates a vision-based object size estimator, an initial parameter guess generated by a large vision-language model (VLM), and a decoupled hierarchical sampling strategy. The visual size estimate and VLM prior offer a strong initial guess of the object's inertial parameters, significantly reducing the search space for sampling-based refinement and improving the overall estimation speed. A hierarchical strategy first estimates mass and center of mass, then infers inertia from object size to ensure physically feasible parameters, while a decoupled multi-hypothesis scheme enhances robustness to VLM prior errors. Our estimator operates in parallel with high-fidelity simulation and hardware, enabling real-time online updates. The estimated parameters are then used to update the wheeled humanoid's equilibrium point, allowing the operator to focus more on locomotion and manipulation. This integration improves the haptic force feedback for dynamic synchronization, enabling more dynamic whole-body teleoperation. By compensating for object dynamics using the estimated parameters, the framework also improves manipulation tracking while preserving compliant behavior. We validate the system on a customized wheeled humanoid with a robotic gripper and human-machine interface, demonstrating real-time execution of lifting, delivering, and releasing tasks with a payload weighing approximately one-third of the robot's body weight.
作者：Donghoon Baek, Amartya Purushottam, Jason J. Choi, Joao Ramos
来源
本页事实对应的公开页面，以原文为准。