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The TACO framework addresses a critical limitation in acrobatic flight control by enabling unified handling of diverse maneuvers and online parameter adjustments. By integrating spectral normalization with input-output rescaling, the method significantly improves policy smoothness and symmetry to bridge the sim-to-real gap. Real-world validation confirms the approach's effectiveness in executing complex tasks like high-speed circular flights and continuous multi-flips.

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TACO: General Acrobatic Flight Control via Target-and-Command-Oriented Reinforcement Learning

arXiv9 iunie 20261 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2503.01125v5 Announce Type: replace Abstract: Although acrobatic flight control has been studied extensively, one key limitation of the existing methods is that they are usually restricted to specific maneuver tasks and cannot change flight pattern parameters online. In this work, we propose a target-and-command-oriented reinforcement learning (TACO) framework, which can handle different maneuver tasks in a unified way and allows online parameter changes. Additionally, we propose a spectral normalization method with input-output rescaling to enhance the policy's temporal and spatial smoothness, independence, and symmetry, thereby overcoming the sim-to-real gap. We validate the TACO approach through extensive simulation and real-world experiments, demonstrating its capability to achieve high-speed circular flights and continuous multi-flips.

Abstract:Although acrobatic flight control has been studied extensively, one key limitation of the existing methods is that they are usually restricted to specific maneuver tasks and cannot change flight pattern parameters online. In this work, we propose a target-and-command-oriented reinforcement learning (TACO) framework, which can handle different maneuver tasks in a unified way and allows online parameter changes. Additionally, we propose a spectral normalization method with input-output rescaling to enhance the policy's temporal and spatial smoothness, independence, and symmetry, thereby overcoming the sim-to-real gap. We validate the TACO approach through extensive simulation and real-world experiments, demonstrating its capability to achieve high-speed circular flights and continuous multi-flips.

Robotics (cs.RO)

arXiv:2503.01125 [cs.RO]

(or arXiv:2503.01125v5 [cs.RO] for this version)

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