DML-RAM: Deep Multimodal Learning Framework for Robotic Arm Manipulation using Pre-trained Models

2025-04-04

AI Summary

Addressing challenges in robotic arm manipulation—including multimodal perception, real-time decision-making, and human-robot collaborative adaptability—this paper proposes a modular, interpretable deep multimodal learning framework. Methodologically, it fuses image sequences (extracted via vision pre-trained models such as VGG16) with robot state data using a late-fusion strategy to jointly regress continuous control actions. Departing from end-to-end black-box and pure reinforcement learning paradigms, the framework integrates lightweight random forest regression to enhance model interpretability while ensuring real-time inference capability. Evaluated on BridgeData V2 and Kuka datasets, it achieves mean squared errors of 0.0021 and 0.0028, respectively, demonstrating high accuracy, robustness, and feasibility for edge deployment. The core contribution is the first integration of an efficient, interpretable regressor into a multimodal late-fusion architecture—uniquely balancing performance, transparency, and low-latency responsiveness to enable adaptive physical human-robot collaboration.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

This paper presents a novel deep learning framework for robotic arm manipulation that integrates multimodal inputs using a late-fusion strategy. Unlike traditional end-to-end or reinforcement learning approaches, our method processes image sequences with pre-trained models and robot state data with machine learning algorithms, fusing their outputs to predict continuous action values for control. Evaluated on BridgeData V2 and Kuka datasets, the best configuration (VGG16 + Random Forest) achieved MSEs of 0.0021 and 0.0028, respectively, demonstrating strong predictive performance and robustness. The framework supports modularity, interpretability, and real-time decision-making, aligning with the goals of adaptive, human-in-the-loop cyber-physical systems.

Problem

• Develops robotic arm control via multimodal deep learning
• Integrates vision and state data for action prediction
• Enables modular, interpretable, real-time manipulation decisions

Innovation

• Deep multimodal learning with late-fusion strategy
• Pre-trained models for image sequence processing
• Modular framework supporting real-time decision-making