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

2026-09-15

AI Summary

为解决30吨级挖掘机的自主控制问题，提出了一种结合模仿学习与自适应笛卡尔跟踪控制的新框架IL-ACT，通过预训练的操作员演示生成关节速度，并利用自适应反馈和增益/偏置估计来修正命令。

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.

Problem

• Autonomous excavator control
• coupled kinematics
• actuation lag
• uncertainty

Innovation

• Imitation Learning
• Adaptive Cartesian Tracking
• Excavator Control
• Gated Gain/Bias Estimation
• Stopping-distance Governor