Coordinating Mobile Bases and Dual-Arm Manipulators via Diffusion Policy for Heavy Door-Opening Control

Manipulating heavy, self-closing doors presents a complex challenge for autonomous agents, requiring tight coupling between dynamic center-of-mass (CoM) shifting and closed-loop force control loops. Traditional data-driven policy frameworks frequently suffer from mode collapse when modeling the highly multi-modal action distributions inherent in these contact-rich, cooperative tasks. To overcome this limitation, current state-of-the-art implementations leverage Diffusion Policy to generate stable action distributions within a continuous control space. This mathematical framework explicitly coordinates a non-holonomic mobile base alongside dual-arm manipulators, enabling the execution of high-dimensional serial commands—such as pulling, pushing, bracing, and hand-over-hand transitions—across a synchronous timeline.

System Architecture and Characterization Deficiencies

The hardware platform configuration requires an integrated system combining a mobile base with dual-arm manipulators to execute macro-micro manipulation. The underlying algorithmic model utilizes conditional diffusion processes to iteratively denoise and synthesize smooth, multi-joint trajectory sequences optimized for non-holonomic navigation and dynamic load balancing.

However, from an industrial integration and deployment perspective, critical technical variables are completely omitted from the current abstract. Key performance indicators—specifically the maximum spring resistance force (measured in Newtons) that the system can actively counter, the real-time model inference frequency (measured in Hertz), and the specific multi-modal sensor fusion schema—remain entirely uncharacterized. For enterprise evaluators looking to deploy mobile manipulators in dynamic commercial or industrial environments, these unlisted parameters determine the boundaries of operational success.

The Inference Latency Bottleneck and Systemic Instability Risks

While Diffusion Policy offers unprecedented flexibility in adapting to non-linear contact scenarios, its iterative sampling architecture introduces a severe computational ceiling during real-time deployment.

• The Computational Latency Trap: High-load, closed-loop force control tasks demand millisecond-level responsiveness to counter abrupt environment counterforces. The fatal vulnerability of diffusion-based control policies resides in high inference latency.
• The Actuation Divergence Risk: When latency exceeds the critical threshold of 100 ms, the system fails to adapt to dynamic loads, inevitably triggering tracking divergence and structural oscillation. Systems relying exclusively on end-to-end vision and behavioral cloning policies cannot substitute for localized, physical joint torque sensor loops. Without hardware-level, high-frequency torque feedback acting as a hard safety mechanism, deploying diffusion policies on heavy hardware remains too unstable for unconstrained commercial facility automation.