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

• URL: http://arxiv.org/abs/2609.00641v1
• Date: Tue, 01 Sep 2026 03:20:24 GMT
• Title: AM-Bench: A Modular Simulation Suite and Benchmark for Aerial Manipulation Policy Learning
• Abstract summary: We introduce AM-Bench, a modular simulation suite and benchmark for multirotor-based aerial manipulation (AM)<n>AM-Bench includes representative embodiments spanning underactuated, fully actuated, and overactuated systems, 12 tasks across contact, transport, and constrained interaction, aerodynamic disturbances and actuator saturation, standard low-level controllers, and baseline policy-learning algorithms.<n>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.
• Score: 22.544699874461582
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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