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

arXiv: https://arxiv.org/abs/2206.14122 · HTML: https://mlsanity.com/paper/2206.14122/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 28 Jun 2022.

## Builds on (most cited first, 13 of 13)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Learning by Cheating](https://mlsanity.com/paper/1912.12294/) (2019)
- [Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks](https://mlsanity.com/paper/1906.08880/) (2019)
- [Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2008.10224/) (2020)
- [Variable impedance control and learning -- A review](https://mlsanity.com/paper/2010.06246/) (2020)
- [Learning Variable Impedance Control for Contact Sensitive Tasks](https://mlsanity.com/paper/1907.07500/) (2019)
- [Stability-Guaranteed Reinforcement Learning for Contact-rich Manipulation](https://mlsanity.com/paper/2004.10886/) (2020)
- [An Omnidirectional Aerial Manipulation Platform for Contact-Based Inspection](https://mlsanity.com/paper/1905.03502/) (2019)
- [Aerial Manipulation Using Hybrid Force and Position NMPC Applied to Aerial Writing](https://mlsanity.com/paper/2006.02116/) (2020)
- [Physical Human-Robot Interaction with a Tethered Aerial Vehicle: Application to a Force-based Human Guiding Problem](https://mlsanity.com/paper/2005.06760/) (2020)
- [Learning dynamics for improving control of overactuated flying systems](https://mlsanity.com/paper/2006.13153/) (2020)

## Abstract (arXiv)

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.