Kinematics-guided multi-task learning for transferable models in robotic manufacturing

Energy‑aware path planning is central to robotic manufacturing, as it demands accurate, low‑latency predictions of both trajectory feasibility and energy consumption. Physics‑based estimators are accurate but slow and platform‑specific, while existing learned surrogates are fast yet often mis‑score infeasible paths and transfer poorly across robot models. We present Kinematics-Guided Multi-Task (KG-MT), a kinematics‑aware architecture that jointly performs prediction of path feasibility and energy consumption during robotic additive manufacturing processes. By injecting inverse kinematics as a shared computational foundation into a shared backbone, KG‑MT internalizes reachability, joint and velocity limits, as well as local Jacobian conditioning, yielding features that benefit both tasks. We tune hyperparameters via Bayesian optimization and study two adaptation regimes, architecture‑only and architecture‑with‑weights transfer, to reduce target‑data needs and training time. Comprehensive evaluations under homogeneous and heterogeneous robot scenarios show that the proposed model not only outperforms traditional two-stage pipelines but also drastically reduces computation time while maintaining high prediction accuracy. In cross-robot transfer tests, KG-MT achieves 97.98 % feasibility accuracy with 2.60 % energy MAE in the homogeneous transfer setting and 96.82 % accuracy with 3.79 % MAE in the heterogeneous setting. Critically, for real-world additive manufacturing applications, KG-MT performs 312 times faster than analytical simulations and 2.2 times faster than cascaded neural network surrogates. KG‑MT provides a practical foundation for cross‑platform, energy‑aware planning in robotic manufacturing, supporting path optimization, robot placement, and sustainable operations, and is readily extensible to additional objectives (e.g., jerk or thermal constraints) without re‑architecting the model.