# Learning Agile Tensile Perching for Aerial Robots from Demonstrations

arXiv: https://arxiv.org/abs/2507.06172 · HTML: https://mlsanity.com/paper/2507.06172/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 13 of its 33 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

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

- [Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations](https://mlsanity.com/paper/1709.10087/) (2017)
- [Overcoming Exploration in Reinforcement Learning with Demonstrations](https://mlsanity.com/paper/1709.10089/) (2017)
- [Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Control](https://mlsanity.com/paper/2103.02142/) (2021)
- [Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges](https://mlsanity.com/paper/1805.00881/) (2018)
- [Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing](https://mlsanity.com/paper/2109.13570/) (2021)
- [Robot Skill Adaptation via Soft Actor-Critic Gaussian Mixture Models](https://mlsanity.com/paper/2111.13129/) (2021)
- [Aerial Tensile Perching and Disentangling Mechanism for Long-Term Environmental Monitoring](https://mlsanity.com/paper/2403.01890/) (2024)
- [Real-Time Trajectory Planning for Aerial Perching](https://mlsanity.com/paper/2203.01061/) (2022)
- [ForestVO: Enhancing Visual Odometry in Forest Environments through ForestGlue](https://mlsanity.com/paper/2504.01261/) (2025)
- [Aerial Robot Control in Close Proximity to Ceiling: A Force Estimation-based Nonlinear MPC](https://mlsanity.com/paper/1907.13594/) (2019)
- [Low-level Pose Control of Tilting Multirotor for Wall Perching Tasks Using Reinforcement Learning](https://mlsanity.com/paper/2108.05457/) (2021)
- [Perch a quadrotor on planes by the ceiling effect](https://mlsanity.com/paper/2307.00861/) (2023)
- [Crash Landing onto "you": Untethered Soft Aerial Robots for Safe Environmental Interaction, Sensing, and Perching](https://mlsanity.com/paper/2405.10043/) (2024)

## Abstract (arXiv)

Perching on structures such as trees, beams, and ledges is essential for extending the endurance of aerial robots by enabling energy conservation in standby or observation modes. A tethered tensile perching mechanism offers a simple, adaptable solution that can be retrofitted to existing robots and accommodates a variety of structure sizes and shapes. However, tethered tensile perching introduces significant modelling challenges which require precise management of aerial robot dynamics, including the cases of tether slack & tension, and momentum transfer. Achieving smooth wrapping and secure anchoring by targeting a specific tether segment adds further complexity. In this work, we present a novel trajectory framework for tethered tensile perching, utilizing reinforcement learning (RL) through the Soft Actor-Critic from Demonstrations (SACfD) algorithm. By incorporating both optimal and suboptimal demonstrations, our approach enhances training efficiency and responsiveness, achieving precise control over position and velocity. This framework enables the aerial robot to accurately target specific tether segments, facilitating reliable wrapping and secure anchoring. We validate our framework through extensive simulation and real-world experiments, and demonstrate effectiveness in achieving agile and reliable trajectory generation for tensile perching.