Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunities (2505.09477v1)

Abstract: The integration of foundation models (FMs) into robotics has enabled robots to understand natural language and reason about the semantics in their environments. However, existing FM-enabled robots primary operate in closed-world settings, where the robot is given a full prior map or has a full view of its workspace. This paper addresses the deployment of FM-enabled robots in the field, where missions often require a robot to operate in large-scale and unstructured environments. To effectively accomplish these missions, robots must actively explore their environments, navigate obstacle-cluttered terrain, handle unexpected sensor inputs, and operate with compute constraints. We discuss recent deployments of SPINE, our LLM-enabled autonomy framework, in field robotic settings. To the best of our knowledge, we present the first demonstration of large-scale LLM-enabled robot planning in unstructured environments with several kilometers of missions. SPINE is agnostic to a particular LLM, which allows us to distill small LLMs capable of running onboard size, weight and power (SWaP) limited platforms. Via preliminary model distillation work, we then present the first language-driven UAV planner using on-device LLMs. We conclude our paper by proposing several promising directions for future research.

Foundation Model-Enabled Robotics: Field Deployment Challenges and Opportunities

The integration of Foundation Models (FMs), notably LLMs and Visual LLMs (VLMs), into robotic systems marks a significant advancement in enabling robots to comprehend and interact with their environments using natural language. The paper "Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunities" focuses on extending these capabilities to field robotics, where robots are required to operate in large-scale, unstructured environments without a complete preexisting map. The research underscores the operational challenges and potential of FM-enabled robotics, aiming to enhance their adaptability and efficiency in real-world scenarios.

Key Contributions

1. SPINE Framework: The paper introduces SPINE, an LLM-enabled autonomy framework, designed to execute complex missions specified in natural language within unknown environments. SPINE facilitates frontier-based online semantic planning, allowing robots to interpret and fulfill incomplete mission specifications by actively exploring the environment.
2. Field Deployments: The authors report deployments of the SPINE framework across various unstructured environments, providing empirical evidence of its capabilities. The deployments showcase kilometer-scale missions, highlighting the system's ability to navigate and plan autonomously in the field.
3. On-device LLMs: Another significant contribution is the development of a process for distilling small LLMs (SLMs) capable of running on resource-constrained platforms. This work addresses the challenge of continuous internet connectivity required by server-based LLMs, presenting initial results of SLM deployment on UAVs.
4. Air-Ground Teaming: The paper presents a framework for air-ground collaboration utilizing shared semantic graphs, which enhances the capability of both UAVs and UGVs in executing language-driven missions. This approach leverages both air and ground perspectives, maximizing environmental understanding and task execution efficiency.

Experimental Insights

The SPINE framework's deployments emphasize the importance of flexible and robust autonomy in field robotics. Key experimental outcomes include:

• Performance Metrics: The framework successfully completed 85% of the missions during field trials, with failure mostly due to external factors such as communication loss. The necessity of online validation to mitigate LLM-generated planning errors was also demonstrated.
• SLM Deployment: The preliminary results of on-device SLM showed a 72.7% success rate in planning tasks, indicating the feasibility of deploying LLMs directly on robots, thereby reducing dependency on external communication infrastructure.

Challenges and Future Directions

The paper identifies several challenges for broadening FM-enabled robotics:

• Communication Infrastructure: Continuous connectivity is crucial for server-based FM operation, posing challenges in remote areas. Advancements in mesh networking or further development in on-device processing are potential areas of focus.
• Model Distillation: Optimizing the distillation process to maintain LLM capabilities in smaller models presents a substantive research challenge. Scaling these models for efficient real-time operation on edge devices remains an open problem.
• Evaluation Protocols: There is a dearth of standardized evaluation datasets and protocols for validating FM-enabled operations in complex, unstructured field environments. Developing these benchmarks will be crucial for advancing field robotics research.

Implications and Speculation

The integration of FMs into robotics promises to revolutionize how robots interact with their environments and users. Practical implications include enhanced adaptability in dynamic environments and the potential for more intuitive human-robot interaction via natural language. Theoretically, this work furthers the understanding of how FMs can be leveraged to bridge the gap between high-level reasoning and low-level robotic control.

Looking ahead, the continued advancement in multimodal foundation models and efficient deployment strategies will likely see broader applications of robots in fields like disaster response, agricultural monitoring, and autonomous exploration, driving a new era in robotics where context-aware and autonomous decision-making become the norm.