Generalist AI Control: Towards Multi-purpose Adaptive Algorithms
Quick Answer
The Generalist Controller introduces a learning-based approach capable of managing diverse dynamic systems without specific tuning.
Quick Take
It utilizes a novel state-space representation with attention mechanisms, achieving performance comparable to system-specific LQI controllers across 25 varied systems, including unstable and non-minimum-phase dynamics. This advancement enables effective control in unseen conditions, marking a significant step towards adaptive algorithms in control systems.
Key Points
- Introduces a Generalist Controller for diverse dynamic systems.
- Utilizes attention mechanisms for a novel state-space representation.
- Achieves performance comparable to LQI controllers across 25 systems.
- Handles challenges like actuator saturation and noise effectively.
- Represents a significant step towards generalist control policies.
DeepSignal Analysis
What happened
The Generalist Controller is a learning-based system designed to manage a variety of dynamic systems without the need for specific tuning. It employs a unique state-space representation with attention mechanisms, achieving performance similar to traditional LQI controllers across 25 diverse systems, including both stable and unstable dynamics.
Key evidence
- The Generalist Controller can control systems of varying orders and dynamics, utilizing a single neural network trained in one shot.
- The model was trained on 314,630 demonstrations from 25 systems, including linear and nonlinear dynamics from various applications.
- Simulation results show that the Generalist Controller performs comparably to system-specific LQI controllers, even in challenging scenarios like non-minimum-phase dynamics.
Why it matters
This development represents a significant advancement in control systems, as it allows for effective management of diverse systems without the need for specific tuning. The ability to generalize across unseen conditions could lead to more robust and adaptable control solutions in various industries, including robotics and aerospace.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Traditional controllers are designed for specific systems and do not transfer across different system orders and dynamics. We present a Generalist Controller, a learning-based controller capable of controlling systems of varying orders and dynamics. The approach introduces a novel dynamic state-space representation using attention mechanisms with masking, enabling a single neural network, trained in one shot, to handle systems with different dimensions without architectural modifications by assigning a system tag to each system. We generated 314,630 demonstrations from 25 diverse systems, including stable, unstable, minimum-phase, and non-minimum-phase dynamics, spanning linear and nonlinear systems from autonomous underwater and aerospace vehicles to mechanical systems and chemical processes. The model learns cross-system control strategies through multi-scale temporal processing and a mixture-of-experts architecture. Simulation results demonstrate that the proposed generalist controller achieves comparable performance to system-specific LQI controllers across all tested systems, including challenging cases such as non-minimum-phase and unstable dynamics, whilst generalising to unseen operating conditions including actuator saturation, noise, disturbance, and reference trajectories not encountered during training. This work represents a significant step towards generalist control policies within a defined family of dynamical systems, demonstrating effective control across a range of single-input single-output (SISO) systems of varying order and dynamics using a single learned policy without system-specific tuning.
| Subjects: | Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Robotics (cs.RO); Systems and Control (eess.SY) |
| Cite as: | arXiv:2607.16313 [cs.AI] |
| (or arXiv:2607.16313v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.16313 arXiv-issued DOI via DataCite (pending registration) |
|
| Journal reference: | Control Engineering Practice 172 (2026) 106915 |
| Related DOI: | https://doi.org/10.1016/j.conengprac.2026.106915
DOI(s) linking to related resources |
Submission history
From: Klinsmann Agyei [view email]
[v1]
Tue, 14 Jul 2026 23:16:38 UTC (850 KB)
— Originally published at arxiv.org
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