一个控制器能通用于多种多旋翼形态，无需重新训练。
Embodiment-conditioned Generalist Control for Multirotor Aerial Robots
- 用归一化动力分配矩阵作为身体特征，统一描述不同构型的运动特性。
- 仅用5分钟训练，可在仿真中控制任意非对称、非平面的多旋翼系统。
- 零样本迁移至三类真实六旋翼机器人，性能稳定可靠。
我们提出一种通用的位置控制策略，可使用同一组网络权重控制任意相同旋翼数的多旋翼构型（如六旋翼或四旋翼）。该策略基于物理合理的身体表征：质量与惯性归一化的动力分配矩阵，捕捉归一化电机推力如何在机体坐标系中产生线加速度和角加速度。通过从广泛的任意多旋翼构型分布中采样（包括非平面和非对称系统），并利用近端策略优化（Proximal Policy Optimization）训练单一紧凑网络，仅需在RTX 3090 GPU上运行五分钟即可完成训练，使用自研NVIDIA Warp动态模拟器。大量仿真实验表明，身体条件化使策略在任意形态下具备鲁棒的通用控制能力。我们在三种不同的六旋翼系统上实现了零样本真实世界迁移，包括平面机器人、部分对称的非平面系统以及随机非对称非平面配置。
原文摘要 · Abstract (English)
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.
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