# Embodiment-conditioned Generalist Control for Multirotor Aerial Robots

arXiv: https://arxiv.org/abs/2606.10857 · HTML: https://mlsanity.com/paper/2606.10857/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 9 Jun 2026.

## Builds on (most cited first, 15 of 15)

- [Control of a Quadrotor with Reinforcement Learning](https://mlsanity.com/paper/1707.05110/) (2017)
- [Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors](https://mlsanity.com/paper/1903.04628/) (2019)
- [One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion](https://mlsanity.com/paper/2409.06366/) (2024)
- [Body Transformer: Leveraging Robot Embodiment for Policy Learning](https://mlsanity.com/paper/2408.06316/) (2024)
- [Learning to Fly in Seconds](https://mlsanity.com/paper/2311.13081/) (2023)
- [Universal Morphology Control via Contextual Modulation](https://mlsanity.com/paper/2302.11070/) (2023)
- [A Learning-based Quadcopter Controller with Extreme Adaptation](https://mlsanity.com/paper/2409.12949/) (2024)
- [Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution](https://mlsanity.com/paper/1911.03098/) (2019)
- [RAPTOR: A Foundation Policy for Quadrotor Control](https://mlsanity.com/paper/2509.11481/) (2025)
- [One Net to Rule Them All: Domain Randomization in Quadcopter Racing Across Different Platforms](https://mlsanity.com/paper/2504.21586/) (2025)
- [GCNT: Graph-Based Transformer Policies for Morphology-Agnostic Reinforcement Learning](https://mlsanity.com/paper/2505.15211/) (2025)
- [Semantics-aware Predictive Inspection Path Planning](https://mlsanity.com/paper/2506.06560/) (2025)
- [The Dodecacopter: a Versatile Multirotor System of Dodecahedron-Shaped Modules](https://mlsanity.com/paper/2504.16475/) (2025)
- [The N-5 Scaling Law: Topological Dimensionality Reduction in the Optimal Design of Fully-actuated Multirotors](https://mlsanity.com/paper/2512.23619/) (2025)
- [Efficient Knowledge Transfer for Jump-Starting Control Policy Learning of Multirotors through Physics-Aware Neural Architectures](https://mlsanity.com/paper/2602.15533/) (2026)

## Abstract (arXiv)

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 RTX 3090 GPU using a custom NVIDIA Warp-based dynamics simulator. Through extensive simulation experiments, we show that embodiment conditioning enables robust generalist control across arbitrary morphologies. We demonstrate zero-shot real-world transfer of this generalist policy on three diverse hexarotor systems, including a planar robot, a partially symmetric non-planar system, and a random asymmetric, non-planar configuration.