Learning Variable Impedance Control for Aerial Sliding on Uneven Heterogeneous Surfaces by Proprioceptive and Tactile Sensing

• URL: http://arxiv.org/abs/2206.14122v1
• Date: Tue, 28 Jun 2022 16:28:59 GMT
• Title: Learning Variable Impedance Control for Aerial Sliding on Uneven Heterogeneous Surfaces by Proprioceptive and Tactile Sensing
• Authors: Weixuan Zhang, Lionel Ott, Marco Tognon, Roland Siegwart
• Abstract summary: We present a learning-based adaptive control strategy for aerial sliding tasks. The proposed controller structure combines data-driven and model-based control methods. Compared to fine-tuned state of the art interaction control methods we achieve reduced tracking error and improved disturbance rejection.
• Score: 42.27572349747162
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The recent development of novel aerial vehicles capable of physically interacting with the environment leads to new applications such as contact-based inspection. These tasks require the robotic system to exchange forces with partially-known environments, which may contain uncertainties including unknown spatially-varying friction properties and discontinuous variations of the surface geometry. Finding a control strategy that is robust against these environmental uncertainties remains an open challenge. This paper presents a learning-based adaptive control strategy for aerial sliding tasks. In particular, the gains of a standard impedance controller are adjusted in real-time by a policy based on the current control signals, proprioceptive measurements, and tactile sensing. This policy is trained in simulation with simplified actuator dynamics in a student-teacher learning setup. The real-world performance of the proposed approach is verified using a tilt-arm omnidirectional flying vehicle. The proposed controller structure combines data-driven and model-based control methods, enabling our approach to successfully transfer directly and without adaptation from simulation to the real platform. Compared to fine-tuned state of the art interaction control methods we achieve reduced tracking error and improved disturbance rejection.

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