AM-Bench: A Modular Simulation Suite and Benchmark for Aerial Manipulation Policy Learning

2026-08-31

AI Summary

本文介绍了AM-Bench，一个用于空中操作策略学习的模块化仿真套件和基准，旨在解决动态关键领域中的独特挑战，通过评估机器人实体、控制及策略之间的交互作用。

Abstract

Standardized benchmarks have played a central role in advancing robot manipulation learning, yet most focus on ground-supported manipulation systems, which limits their applicability to dynamics-critical domains such as aerial manipulation (AM). AM presents distinct system-level challenges, including environmental disturbances, coupled dynamics between the manipulator and floating base, and constrained degrees of freedom. Consequently, task performance depends jointly on robot embodiment, low-level control, and high-level policy design. We introduce AM-Bench, a modular simulation suite and benchmark for multirotor-based AM policy learning. AM-Bench includes representative embodiments spanning underactuated, fully actuated, and overactuated systems, 12 tasks across contact, transport, and constrained interaction, configurable aerodynamic disturbances and actuator saturation, standard low-level controllers, and baseline policy-learning algorithms. Unlike prior manipulation benchmarks that primarily emphasize end-to-end policy performance, AM-Bench enables system-level evaluation of how embodiment, control, disturbances, and policy choices interact. We demonstrate its diagnostic value through three simulation studies spanning high-level policies, policy--control interfaces, and embodiments, together with real-world validation of modeled effects and a hardware test of the learning pipeline.

Problem

• aerial manipulation
• dynamics-critical domains
• system-level challenges

Innovation

• aerial manipulation
• modular simulation suite
• system-level evaluation
• policy learning
• multirotor-based