arxiv_cv 90% Match Research Paper Robotics engineers,AI researchers,Edge computing specialists,Mobile robot developers 20 hours ago

From the Laboratory to Real-World Application: Evaluating Zero-Shot Scene Interpretation on Edge Devices for Mobile Robotics

robotics › sim-to-real

📄 Abstract

Abstract: Video Understanding, Scene Interpretation and Commonsense Reasoning are highly challenging tasks enabling the interpretation of visual information, allowing agents to perceive, interact with and make rational decisions in its environment. Large Language Models (LLMs) and Visual Language Models (VLMs) have shown remarkable advancements in these areas in recent years, enabling domain-specific applications as well as zero-shot open vocabulary tasks, combining multiple domains. However, the required computational complexity poses challenges for their application on edge devices and in the context of Mobile Robotics, especially considering the trade-off between accuracy and inference time. In this paper, we investigate the capabilities of state-of-the-art VLMs for the task of Scene Interpretation and Action Recognition, with special regard to small VLMs capable of being deployed to edge devices in the context of Mobile Robotics. The proposed pipeline is evaluated on a diverse dataset consisting of various real-world cityscape, on-campus and indoor scenarios. The experimental evaluation discusses the potential of these small models on edge devices, with particular emphasis on challenges, weaknesses, inherent model biases and the application of the gained information. Supplementary material is provided via the following repository: https://datahub.rz.rptu.de/hstr-csrl-public/publications/scene-interpretation-on-edge-devices/

Key Contributions

Investigates the capabilities of state-of-the-art VLMs for scene interpretation and action recognition on edge devices for mobile robotics. It focuses on evaluating smaller VLMs that balance accuracy and inference time for real-world robotic applications, addressing the computational challenges of deploying large models.

Business Value

Enables more intelligent and adaptable mobile robots capable of understanding their environment and actions in real-time, even with limited onboard processing power, leading to safer and more versatile robotic applications.

Paper Metadata

Innovation Type

Evaluation and Application

Deployment Feasibility

Focuses specifically on deployment feasibility on edge devices for mobile robotics, evaluating the trade-offs involved.

Limitations Addressed

High computational complexity of LLMs/VLMs,Challenges in deploying advanced models on resource-constrained edge devices,Trade-off between accuracy and inference speed,Need for zero-shot capabilities in robotics

Technical Tags

Zero-Shot Scene InterpretationEdge DevicesMobile RoboticsLarge Language Models (LLMs)Visual Language Models (VLMs)Scene InterpretationCommonsense ReasoningAction RecognitionInference TimeAccuracy Trade-offSmall VLMsReal-world Application

Research Topics

RoboticsArtificial IntelligenceComputer VisionNatural Language ProcessingEdge Computing

Methods & Architectures

Zero-shot learningEvaluation of VLMs on edge devicesScene interpretationAction recognition Large Language Models (LLMs)Visual Language Models (VLMs)

Applications & Tasks

Mobile Robotics Edge AI Autonomous Systems Human-Robot Interaction Computational complexity of LLMs/VLMs for edge devicesTrade-off between accuracy and inference timeApplying advanced vision-language models to robotics tasksZero-shot generalization for scene interpretation and action recognition Scene InterpretationAction RecognitionCommonsense ReasoningZero-shot learning

Related Fields

RoboticsArtificial IntelligenceEdge ComputingComputer VisionNatural Language ProcessingEmbodied AI

Keywords

roboticsedge devicesLLMVLMscene interpretationaction recognitionzero-shotmobile roboticsinference timecomputational complexityembodied AI

Academic Context

#Robotics#Artificial Intelligence#Computer Vision#Natural Language Processing#Edge Computing

Commercial Potential

Potential Products

Onboard AI modules for mobile robotsRobotic perception and reasoning systemsEdge AI platforms for robotics

Target Industries

RoboticsAutomotiveLogisticsManufacturingConsumer Electronics

Use Case Examples

Autonomous mobile robots navigating complex environmentsRobots performing tasks based on visual scene understandingHuman-robot collaboration requiring real-time interpretation

Competitive Edge

Addresses the practical challenge of deploying advanced vision-language models on resource-constrained edge devices for robotics, focusing on the accuracy-inference time trade-off rather than solely on achieving state-of-the-art accuracy on large-scale benchmarks.

Market Opportunity

Rapidly growing market for autonomous mobile robots and edge AI solutions.

Revenue Models

Licensing of AI modulesintegration servicesspecialized robotic hardware.

Resource Requirements

Compute Needs

Focuses on low compute requirements suitable for edge devices.

Data Requirements

Requires diverse datasets for evaluating scene interpretation and action recognition, potentially including robotic interaction data.

Deployment Constraints

Limited computational power on edge devices,Real-time processing needs,Power consumption constraints

Scalability

Focuses on scaling down large models to fit edge devices, rather than scaling up.

Production Readiness

Maturity Level

Research

Time to Market

1-3 years

Patent Potential

Moderate, for efficient VLM architectures or deployment strategies for edge robotics.

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