SOURCE-LINKED INTELLIGENCE
IL-ACT: Imitation Learning with Adaptive Cartesian Tracking Control for a 30-ton Excavator
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,
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-15T06:22:10.000Z
First collected: 2026-09-20T09:01:24.920Z. This is not the publication date.