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arxiv_ai 90% Match Thesis/Dissertation Robotics Researchers,AI Researchers,Robotics Engineers,Foundation Model Developers 2 days ago

Leveraging Foundation Models for Enhancing Robot Perception and Action

robotics › manipulation
📄 Abstract

Abstract: This thesis investigates how foundation models can be systematically leveraged to enhance robotic capabilities, enabling more effective localization, interaction, and manipulation in unstructured environments. The work is structured around four core lines of inquiry, each addressing a fundamental challenge in robotics while collectively contributing to a cohesive framework for semantics-aware robotic intelligence.
Authors (1)
Reihaneh Mirjalili
Submitted
October 30, 2025
arXiv Category
cs.RO
arXiv PDF

Key Contributions

Investigates the systematic application of foundation models to enhance robot perception and action capabilities, aiming to enable more effective localization, interaction, and manipulation in complex, unstructured environments. It proposes a cohesive framework for developing semantics-aware robotic intelligence.

Business Value

Paves the way for more capable and versatile robots in diverse applications, from manufacturing and logistics to potentially domestic assistance, by leveraging the power of large pre-trained models.

Paper Metadata

Innovation Type

Framework and Methodology

Deployment Feasibility

Moderate to High, depending on the specific foundation models and robotic platforms used. Integration can be complex.

Limitations Addressed

Challenges in robot perception and action in unstructured environments, and the need for more intelligent, adaptable robotic systems.

Technical Tags

foundation modelsrobot perceptionrobot actionunstructured environmentslocalizationinteractionmanipulationsemantics-aware AI

Research Topics

Foundation Models in RoboticsRobot Perception and ActionEmbodied AIRobotics IntelligenceHuman-Robot Interaction

Methods & Architectures

Foundation ModelsSystematic Leveraging Foundation Models

Applications & Tasks

Robotics Autonomous Systems Industrial Automation Logistics Domestic Robots Robot PerceptionRobot Action PlanningNavigation in Unstructured EnvironmentsObject InteractionSemantic Understanding for Robots Robot LocalizationRobot InteractionRobot ManipulationPerception Enhancement

Related Fields

Artificial IntelligenceMachine LearningRoboticsComputer VisionNatural Language Processing

Keywords

Foundation ModelsRoboticsPerceptionActionManipulationLocalizationInteractionUnstructured EnvironmentsEmbodied AISemanticsAI Framework

Academic Context

#Foundation Models in Robotics#Robot Perception and Action#Embodied AI#Robotics Intelligence#Human-Robot Interaction

Commercial Potential

Potential Products
Advanced robotic systems for logisticsNext-generation industrial robotsService robots for complex environments
Target Industries
ManufacturingWarehousingLogisticsAutomotiveHealthcare
Use Case Examples
Robots that can autonomously navigate and manipulate objects in a cluttered warehouseService robots capable of assisting humans in dynamic environmentsAutonomous systems for complex assembly tasks
Competitive Edge
Represents a significant step towards integrating state-of-the-art foundation models into robotics, potentially leapfrogging traditional approaches that require extensive task-specific training.
Market Opportunity
Rapidly growing market for advanced robotics and automation.
Revenue Models
Robotics hardware salessoftware licensingservice contracts.

Resource Requirements

Compute Needs
High for training/fine-tuning foundation models, moderate to high for robot inference.
Data Requirements
Large and diverse datasets for training/fine-tuning foundation models, and real-world robot interaction data.
Deployment Constraints
Real-time performance, safety, robustness in dynamic environments, computational resources on-board robots.
Scalability
Scalability depends on the foundation models used and the efficiency of their deployment on robotic hardware.
Regulatory Considerations
Safety standards for autonomous robotsespecially in human-centric environments.

Production Readiness

Maturity Level

Research/Development

Time to Market

2-5 years for advanced applications.

Patent Potential

Moderate to High for novel integration methods and robotic control strategies.

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