Joint-State Inference for Autonomous Excavators Using Cab-View Vision-Control Fusion
Overview
This research presents a closed-loop autonomous excavation framework that infers excavator joint states using onboard visual observations and control-related temporal information without relying on dedicated joint sensors or external cameras. The method encodes structural cues from cab-view images, models short-term motion trends from historical joystick commands, and incorporates mechanical joint-range constraints to ensure feasible estimates. Evaluated in an AGX Dynamics for Unity simulation environment across 20 autonomous excavation trials, the framework achieved a 95% task-completion rate with an average latency of 10.11 ms per step and a mean absolute error of 0.965 degrees for bucket-joint inference.
Generated from attributed reports · Updated 4 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Environment: simulation
“ible 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 r”
Exact source · revision 1Source owner not reported
Control: reported autonomous
“arXiv:2611.13690v1 Announce Type: new 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 o”
Exact source · revision 1Source owner not reported
Reported quantity · latency per step: 10.11 other · Basis not reported
“ate, 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.”
Exact source · revision 1Source owner not reported
Reported quantity · MAE for bucket-joint inference: 0.965 other · Basis not reported
“ate, 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.”
Exact source · revision 1Source owner not reported
Reported quantity · trials: 20 other · reported trials
“ible 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 r”
Exact source · revision 1Source owner not reported
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalJoint-State Inference for Autonomous Excavators Using Cab-View Vision-Control Fusion
This research presents a closed-loop autonomous excavation framework that infers excavator joint states using onboard visual observations and control-related temporal information without relying on dedicated joint sensors or external cameras. The method encodes structural cues from cab-view images, models short-term motion trends from historical joystick commands, and incorporates mechanical joint-range constraints to ensure feasible estimates. Evaluated in an AGX Dynamics for Unity simulation environment across 20 autonomous excavation trials, the framework achieved a 95% task-completion rate with an average latency of 10.11 ms per step and a mean absolute error of 0.965 degrees for bucket-joint inference.
Event coverage history
There is not enough continuous observation data to show a trend.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).