外观
论文arXiv 2609.205581 个月前
面向屋面施工的人形机器人坡度自适应全身运动学习
针对坡屋面施工中的全身运动学习，提出任务语义、场景锚定框架，并在Unitree G1的跟踪、跨任务和实物实验中验证。
宇树 G1原文链接：Learning Slope-Adaptive Whole-Body Locomotion for Humanoid Robots in Roofing Construction（arXiv 2609.20558 · 发布日期：2026-09-17）
Learning Slope-Adaptive Whole-Body Locomotion for Humanoid Robots in Roofing Construction
针对坡屋面施工中的全身运动学习，提出任务语义、场景锚定框架，并在Unitree G1的跟踪、跨任务和实物实验中验证。
核心要点
- 坡屋面作业要求移动、平衡与作业动作协调，直接重定向易使手脚相对屋面位置错误。
- 方法结合人体示范、度量屋面模型、轨迹级优化与执行感知强化学习，锚定支撑和作业关系。
- 在多动作跟踪、屋面坡度覆盖、钉枪消融及锤击、侧推跨任务实验中，与基线比较。
- 各任务3/3成功，作业间隙误差0.256至0.531 cm；实物实验平均基座系运动误差低于80 mm。
验证范围与局限：验证限于Unitree G1上的屋面相关动作；实物实验复现上坡行走、钉枪、锤击和弯腰。
英文摘要 (Abstract)
Roofing requires workers to coordinate locomotion, balance, and work-related body motions on pitched surfaces, creating a challenging application for humanoid robots. Directly retargeted human demonstrations, however, may preserve motion appearance while placing the robot's feet or hands incorrectly relative to the roof. This study presents a task-semantic scene-grounded framework for learning roofer-style whole-body motions on a Unitree G1. Human demonstrations are captured using a tracking system and retargeted to the robot, while a metric roof model supplies the spatial reference unavailable from the tracking system. A trajectory-level optimization grounds inferred support contacts and annotated work relations to the roof, and execution-aware reinforcement learning encourages the resulting policy to preserve these relations under dynamic tracking errors. The framework is evaluated through a multi-motion tracking study, a roof-pitch coverage matrix, a five-way nailgun ablation, cross-task experiments on hammering and lateral pushing, and comparisons with pure reinforcement learning and zero-shot teleoperation. Our method enables the robot to satisfy support, work-clearance, and nonpenetration criteria across all evaluated seeds. Across nailgun, hammering, and pushing, it achieves work-clearance errors between 0.256 and 0.531 cm and 3/3 successful evaluations per task. Physical experiments reproduce uphill walking, nailgun, hammering, and bending motions with mean base-frame motion errors below 80 mm. These findings establish scene-grounded human motion learning as a promising basis for construction-oriented humanoid motion primitives.
作者：Songyang Liu, Shuai Li
来源
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