论文/arXiv 2609.16696·1 个月前

IL-ACT：用于30吨挖掘机的带自适应笛卡尔跟踪控制的模仿学习

面向受耦合运动学、执行滞后和不确定性影响的自主挖掘机控制，提出IL-ACT并在Simscape中验证。

AI 整理

• 本体 SDK 与控制
• 数据与采集
• 具身模型

原文链接：IL-ACT: Imitation Learning with Adaptive Cartesian Tracking Control for a 30-ton Excavator（arXiv 2609.16696 · 发布日期：2026-09-15）

IL-ACT: Imitation Learning with Adaptive Cartesian Tracking Control for a 30-ton Excavator

面向受耦合运动学、执行滞后和不确定性影响的自主挖掘机控制，提出IL-ACT并在Simscape中验证。

核心要点

• 自主挖掘机控制受耦合运动学、执行滞后和不确定性挑战。
• 锚定14输入模仿策略生成关节速率，并由自适应笛卡尔反馈等修正。
• 在Simscape的100个顺序目标及多种轨迹中，IL-ACT较Teacher+ACT更快且终端误差更低。
• 预训练权重影响仍不一致，原教师对比存在螺旋RMSE与最大误差权衡。

验证范围与局限：验证范围为Simscape仿真中的顺序目标和螺旋、8字、圆角光栅跟踪任务。

英文摘要 (Abstract)

Autonomous excavator control is challenged by coupled kinematics, actuation lag, and uncertainty. We propose imitation learning and adaptive Cartesian tracking (IL-ACT), a novel motion control framework for a 30-ton-class excavator. An anchored, 14-input imitation policy pretrained on operator demonstrations generates nominal joint rates; adaptive Cartesian feedback and gated gain/bias estimation correct these commands before a stopping-distance governor constrains joint-reference generation. Simscape evaluation covers 100 sequential goals and spiral, figure-eight, and rounded-raster tracking, including 88 additional runs across three training seeds, two initializations, and speeds, under hydraulic response and sensing conditions. Compared with Teacher+ACT, IL-ACT completes all goals with shorter duration and lower terminal errors under both response conditions. Telemetry-initialized IL-ACT lowers RMSE in all 24 figure-eight and rounded-raster seed comparisons and lowers additional-load spiral mean RMSE by approximately 29%. Original spiral RMSE also improves over IL-only and PID. Under a shared sensor-noise realization, telemetry-initialized IL-ACT achieves 27.67% lower mean RMSE than Teacher+ACT; enabling estimation reduces mean RMSE by $22.44%$ relative to the frozen estimator. Pretrained-weight effects remain mixed, and the original teacher comparison exhibits a spiral RMSE--maximum-error tradeoff. Analysis establishes bounded adaptive states and Cartesian feedback, with reference admissibility conditional on governor feasibility.

作者：Mehdi Heydari Shahna, Seihun Kim, Soyi Jung, Soohyun Park, Jouni Mattila, Joongheon Kim

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2609.16696：IL-ACT: Imitation Learning with Adaptive Cartesian Tracking Control for a 30-ton Excavator访问 2026-10-21