Embodiment-conditioned Generalist Control for Multirotor Aerial Robots
We present a generalist position control policy capable of controlling arbitrary multirotor configurations of a certain rotor count (e.g., hexarotors or quadrotors) with a single set of network weights. The policy is conditioned on a physics-grounded embodiment descriptor: a mass and inertia-normalized control allocation matrix that captures how mass-normalized motor thrusts generate linear and angular accelerations in the body-frame. To train the policy, we sample from a broad distribution of arbitrary multirotor configurations, including non-planar and asymmetric systems, and optimize a single, compact network using Proximal Policy Optimization. Training requires only five minutes on an...
| Authors | Orestis Konstantaropoulos, Welf Rehberg, Mihir Kulkarni et al. |
|---|---|
| Primary category | cs.RO |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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