Kavli Affiliate: Morteza Gharib
| First 5 Authors: Ioannis Mandralis, Richard M. Murray, Morteza Gharib, ,
| Summary:
Quadrotor Morpho-Transition, or the act of transitioning from air to ground
through mid-air transformation, involves complex aerodynamic interactions and a
need to operate near actuator saturation, complicating controller design. In
recent work, morpho-transition has been studied from a model-based control
perspective, but these approaches remain limited due to unmodeled dynamics and
the requirement for planning through contacts. Here, we train an end-to-end
Reinforcement Learning (RL) controller to learn a morpho-transition policy and
demonstrate successful transfer to hardware. We find that the RL control policy
achieves agile landing, but only transfers to hardware if motor dynamics and
observation delays are taken into account. On the other hand, a baseline MPC
controller transfers out-of-the-box without knowledge of the actuator dynamics
and delays, at the cost of reduced recovery from disturbances in the event of
unknown actuator failures. Our work opens the way for more robust control of
agile in-flight quadrotor maneuvers that require mid-air transformation.
| Search Query: ArXiv Query: search_query=au:”Morteza Gharib”&id_list=&start=0&max_results=3