IL-ACT: Imitation Learning with Adaptive Cartesian Tracking Control for a 30-ton Excavator
On the map: Offline RL and imitation learning from data → Imitation learning methods
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 17 of its 25 references that are in the map. First posted on arXiv 15 Sep 2026.
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17 of 25 references.
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- 2024Contractive Dynamical Imitation Policies for Efficient Out-of-Sample Recovery10 Dec
- 2024Stable-BC: Controlling Covariate Shift with Stable Behavior Cloning12 Aug
- 2019Causal Confusion in Imitation Learning28 May
Other references · 14
- 2021What Matters in Learning from Offline Human Demonstrations for Robot Manipulationcited by 699
- 2024How Generalizable Is My Behavior Cloning Policy? A Statistical Approach to Trustworthy Performance Evaluationcited by 14
- 2023Stable and Safe Reinforcement Learning via a Barrier-Lyapunov Actor-Critic Approachcited by 11
- 2025MTIL: Encoding Full History with Mamba for Temporal Imitation Learningcited by 10
- 2020Optimization-Based Framework for Excavation Trajectory Generationcited by 5
- 2021Real-Time Motion Planning of a Hydraulic Excavator using Trajectory Optimization and Model Predictive Controlcited by 2
- 2024Efficient Model Learning and Adaptive Tracking Control of Magnetic Micro-Robots for Non-Contact Manipulationcited by 2
- 2017Full- and Reduced-order Model of Hydraulic Cylinder for Motion Controlcited by 1
- 2023Exponential Auto-Tuning Fault-Tolerant Control of N Degrees-of-Freedom Manipulators Subject to Torque Constraintscited by 1
- 2024Robust Sensor-Limited Control with Safe Input-Output Constraints for Hydraulic In-Wheel Motor Drive Mobility Systemscited by 1
- 2024Robust Model-Free Control Framework with Safety Constraints for a Fully Electric Linear Actuator Systemcited by 1
- 2025Physics-informed Neural Network Predictive Control for Quadruped Locomotioncited by 1
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- 2024ExACT: An End-to-End Autonomous Excavator System Using Action Chunking With Transformers
- 2026LOPAL: Local Performance-Aware Active Learning from Imperfect Demonstrations
- 2025Feasibility-aware Imitation Learning from Observations through a Hand-mounted Demonstration Interface
- 2023Learning Excavation of Rigid Objects with Offline Reinforcement Learning
- 2026SwingRL: Adaptive Observation Reinforcement Learning with World-Model Prediction for Cable-Suspended Hoisting Control
- 2025Sim-to-Real Dynamic Object Manipulation on Conveyor Systems via Optimization Path Shaping
- 2022Disturbance-Injected Robust Imitation Learning with Task Achievement
- 2025RISE: Robust Imitation through Stochastic Encoding