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

arXiv 2504.03423 ↗First posted 4 Apr 2025cs.LG cs.ROcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 13 references that are in the map. First posted on arXiv 4 Apr 2025.

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References · 11

  1. 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 730
  2. 2023BridgeData V2: A Dataset for Robot Learning at Scalecited by 634
  3. 2016Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collectioncited by 312
  4. 2018Sim-to-Real: Learning Agile Locomotion For Quadruped Robotscited by 277
  5. 2016Unsupervised Learning for Physical Interaction through Video Predictioncited by 256
  6. 2022CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigationcited by 194
  7. 2018Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulationcited by 140
  8. 2023Task and Motion Planning with Large Language Models for Object Rearrangementcited by 140
  9. 2022Visuo-Tactile Transformers for Manipulationcited by 43
  10. 2016Learning to Push by Grasping: Using multiple tasks for effective learningcited by 36
  11. 2020Zero-Shot Human-Object Interaction Recognition via Affordance Graphscited by 3

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