Learning End-to-End Control for Omnidirectional Aerial Motion on Overactuated Tilt-rotor Quadrotors

arXiv:2602.21583v2 Announce Type: replace Abstract: While reinforcement learning (RL) has been successfully applied to conventional quadrotors for agile and robust flight, whether actuator-level RL can be reliably deployed on tilt-rotor aerial robots remains an open question, as the hybrid actuation coupling brushless rotors with rotational joints introduces a substantially harder sim-to-real gap. In this work, we propose an end-to-end RL framework for omnidirectional motion control on overactuated tilt-rotor quadrotors, directly mapping target poses to joint and rotor commands. The learning framework combines actuator-level simulation with an asymmetric actor-critic architecture for 6D pose-reaching. For reliable sim-to-real transfer on the hybrid actuation, we integrate system identification with minimal yet physically grounded domain randomization. The trained policy is deployed zero-shot on real hardware and evaluated across waypoint hovering, external disturbances, payload variation and trajectory tracking, together with simulated traversal of allocation-singular configurations. The policy is compared with a state-of-the-art NMPC baseline: NMPC attains lower steady-state position error, whereas the RL policy offers a more uniform orientation error across evaluations, transitions between poses faster, and requires less onboard computation.

Abstract:While reinforcement learning (RL) has been successfully applied to conventional quadrotors for agile and robust flight, whether actuator-level RL can be reliably deployed on tilt-rotor aerial robots remains an open question, as the hybrid actuation coupling brushless rotors with rotational joints introduces a substantially harder sim-to-real gap. In this work, we propose an end-to-end RL framework for omnidirectional motion control on overactuated tilt-rotor quadrotors, directly mapping target poses to joint and rotor commands. The learning framework combines actuator-level simulation with an asymmetric actor-critic architecture for 6D pose-reaching. For reliable sim-to-real transfer on the hybrid actuation, we integrate system identification with minimal yet physically grounded domain randomization. The trained policy is deployed zero-shot on real hardware and evaluated across waypoint hovering, external disturbances, payload variation and trajectory tracking, together with simulated traversal of allocation-singular configurations. The policy is compared with a state-of-the-art NMPC baseline: NMPC attains lower steady-state position error, whereas the RL policy offers a more uniform orientation error across evaluations, transitions between poses faster, and requires less onboard computation.

Robotics (cs.RO)

arXiv:2602.21583 [cs.RO]

(or arXiv:2602.21583v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2602.21583 Focus to learn more arXiv-issued DOI via DataCite

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Learning End-to-End Control for Omnidirectional Aerial Motion on Overactuated Tilt-rotor Quadrotors

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