Abstract
Reliable state feedback of excavator front-end mechanisms is essential for autonomous excavation, yet retrofitting existing machines with dedicated joint sensors, external cameras, or visual markers can increase deployment and maintenance burdens. This paper proposes a closed-loop autonomous excavation framework for continuous inference of excavator joint states using onboard visual observations and control-related temporal information. The framework encodes structural cues from the current cab-view image, models short-term motion trends from historical joystick commands, and introduces a rolling state buffer for the bucket joint to preserve temporal continuity under severe occlusion. Mechanical joint-range constraints are incorporated into the learning objective to suppress infeasible estimates. The current cab-view image and inferred states are supplied to an Action Chunking with Transformers (ACT)-based control policy for online action generation. Experiments in an AGX Dynamics for Unity simulation environment under diverse operating conditions evaluate joint-state inference and end-to-end task execution. Across 20 autonomous excavation trials, the framework achieved a 95% task-completion rate, with an average joint-state-inference-and-control latency of 10.11 ms per step. The bucket-joint inference achieved a mean absolute error (MAE) of 0.965 degrees. These results demonstrate a sensor-light and computationally efficient joint-state-inference approach that can support closed-loop autonomous excavation with reduced dependence on additional sensing infrastructure.
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URL
https://arxiv.org/abs/2611.13690
