Learning to Open Doors with an Aerial Manipulator

TL;DR

This paper introduces a reinforcement learning approach for an aerial robot to open doors, demonstrating improved robustness and speed over traditional control methods in both simulation and real-world tests.

Contribution

The paper presents a novel RL-based method for aerial manipulation, specifically for door opening, with successful transfer from simulation to real hardware.

Findings

RL policy outperforms MPPI in robustness and speed

Successful real-world deployment of the learned policy

Enhanced disturbance tolerance in manipulation tasks

Abstract

The field of aerial manipulation has seen rapid advances, transitioning from push-and-slide tasks to interaction with articulated objects. So far, when more complex actions are performed, the motion trajectory is usually handcrafted or a result of online optimization methods like Model Predictive Control (MPC) or Model Predictive Path Integral (MPPI) control. However, these methods rely on heuristics or model simplifications to efficiently run on onboard hardware, producing results in acceptable amounts of time. Moreover, they can be sensitive to disturbances and differences between the real environment and its simulated counterpart. In this work, we propose a Reinforcement Learning (RL) approach to learn motion behaviors for a manipulation task while producing policies that are robust to disturbances and modeling errors. Specifically, we train a policy to perform a door-opening task with an Omnidirectional Micro Aerial Vehicle (OMAV). The policy is trained in a physics simulator and experiments are presented both in simulation and running onboard the real platform, investigating the simulation to real world transfer. We compare our method against a state-of-the-art MPPI solution, showing a considerable increase in robustness and speed.