# 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

> 原文链接：https://robotics.hellobike.com/papers/2026-09-15-2609-16696
> 频道：papers | 更新日期：2026-09-15 | 标签：arxiv, cs.RO
> 核心摘要：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。

# 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

::: v-pre
> 原文链接：[IL-ACT: Imitation Learning with Adaptive Cartesian Tracking Control for a 30-ton Excavator](https://arxiv.org/abs/2609.16696)（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
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## 原始参考来源 (Sources)

- [arXiv 2609.16696：IL-ACT: Imitation Learning with Adaptive Cartesian Tracking Control for a 30-ton Excavator](https://arxiv.org/abs/2609.16696)