[Paper] Embedding Autonomous Agents in Resource-Constrained Robotic Platforms

Source: arXiv - 2601.04191v1

Overview

The paper demonstrates that a high‑level autonomous software agent can run on a tiny, low‑power robot and still make real‑time decisions. By embedding an AgentSpeak‑based reasoning engine into a two‑wheeled platform, the authors show that even severely resource‑constrained hardware can navigate a maze autonomously, opening the door for smarter edge devices in robotics, IoT, and embedded AI.

Key Contributions

• First‑hand integration of an AgentSpeak autonomous agent with a minimal two‑wheeled robot (≈ 10 cm, < 100 g).
• Quantitative performance data: the robot solved a maze in 59 s using only 287 reasoning cycles; each decision took < 1 ms.
• Proof‑of‑concept that high‑level BDI (Belief‑Desire‑Intention) reasoning can meet real‑time constraints on embedded hardware.
• Open‑source implementation (agent code, robot firmware, and experimental scripts) to encourage reproducibility and further research.
• Guidelines for mapping high‑level agent constructs onto low‑level sensor/actuator loops in resource‑tight platforms.

Methodology

1. Platform selection – A commercially available two‑wheeled robot equipped with a low‑cost microcontroller (16 MHz, 32 KB RAM) and basic proximity sensors.
2. Agent architecture – The authors used AgentSpeak, a declarative language for BDI agents, to encode the robot’s beliefs (sensor readings), desires (reach the maze exit), and intentions (navigate forward, turn left/right).
3. Embedding process – The AgentSpeak interpreter was cross‑compiled to run directly on the microcontroller, interfacing with a thin hardware abstraction layer that translates agent actions into motor commands and sensor updates.
4. Experimental setup – A 3 m × 3 m maze with multiple branches was built; the robot started at a fixed entry point and had to locate the exit without external guidance.
5. Metrics collected – Total execution time, number of reasoning cycles, per‑cycle CPU time, and memory footprint were logged via a lightweight telemetry module.

Results & Findings

These numbers prove that the BDI reasoning loop is lightweight enough for real‑time control on a microcontroller with sub‑100 KB RAM. The robot’s behavior remained deterministic and robust despite sensor noise, indicating that the high‑level agent model can tolerate typical embedded uncertainties.

Practical Implications

• Edge AI for robotics – Developers can now embed sophisticated decision‑making (goal‑oriented planning, reactive behaviors) directly on cheap robots, eliminating the need for constant cloud off‑loading.
• IoT autonomy – Similar BDI agents could be deployed on smart sensors, drones, or wearables that must act locally under strict power budgets.
• Rapid prototyping – Using AgentSpeak as a high‑level language lets engineers prototype complex behaviors without writing low‑level control code, then compile the same logic onto constrained hardware.
• Safety‑critical systems – Predictable, bounded reasoning times (< 1 ms) satisfy many real‑time safety standards, making the approach viable for warehouse AGVs, delivery bots, or assistive devices.
• Scalable fleet management – Each robot can make local navigation decisions while a central server coordinates higher‑level tasks, reducing network traffic and latency.

Limitations & Future Work

• Hardware scope – Experiments were limited to a single microcontroller class; performance on even tighter platforms (e.g., sub‑8 KB RAM) remains untested.
• Complexity ceiling – The maze task is relatively simple; scaling to richer environments (dynamic obstacles, multi‑robot coordination) may increase reasoning cycles beyond the demonstrated limits.
• Energy profiling – While CPU load was measured, the paper does not provide a detailed power consumption analysis, which is crucial for battery‑operated deployments.
• AgentSpeak extensions – Future work could explore integrating learning components (e.g., reinforcement learning) with the BDI model to adapt behaviors on‑the‑fly while preserving real‑time guarantees.

Overall, the study offers a compelling blueprint for bringing high‑level autonomous agents to the edge, showing that “thinking” robots need not be power‑hungry or cloud‑dependent.

Authors

• Negar Halakou
• Juan F. Gutierrez
• Ye Sun
• Han Jiang
• Xueming Wu
• Yilun Song
• Andres Gomez

Paper Information

• arXiv ID: 2601.04191v1
• Categories: cs.RO, cs.AI
• Published: January 7, 2026
• PDF: Download PDF