A Multi-Modal Interaction Framework for Efficient Human-Robot Collaborative Shelf Picking
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917981194289152 |
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| author | Pathak, Abhinav Venkatesan, Kalaichelvi Taha, Tarek Muthusamy, Rajkumar |
| author_facet | Pathak, Abhinav Venkatesan, Kalaichelvi Taha, Tarek Muthusamy, Rajkumar |
| contents | The growing presence of service robots in human-centric environments, such as warehouses, demands seamless and intuitive human-robot collaboration. In this paper, we propose a collaborative shelf-picking framework that combines multimodal interaction, physics-based reasoning, and task division for enhanced human-robot teamwork.
The framework enables the robot to recognize human pointing gestures, interpret verbal cues and voice commands, and communicate through visual and auditory feedback. Moreover, it is powered by a Large Language Model (LLM) which utilizes Chain of Thought (CoT) and a physics-based simulation engine for safely retrieving cluttered stacks of boxes on shelves, relationship graph for sub-task generation, extraction sequence planning and decision making. Furthermore, we validate the framework through real-world shelf picking experiments such as 1) Gesture-Guided Box Extraction, 2) Collaborative Shelf Clearing and 3) Collaborative Stability Assistance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06593 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Multi-Modal Interaction Framework for Efficient Human-Robot Collaborative Shelf Picking Pathak, Abhinav Venkatesan, Kalaichelvi Taha, Tarek Muthusamy, Rajkumar Robotics Human-Computer Interaction The growing presence of service robots in human-centric environments, such as warehouses, demands seamless and intuitive human-robot collaboration. In this paper, we propose a collaborative shelf-picking framework that combines multimodal interaction, physics-based reasoning, and task division for enhanced human-robot teamwork. The framework enables the robot to recognize human pointing gestures, interpret verbal cues and voice commands, and communicate through visual and auditory feedback. Moreover, it is powered by a Large Language Model (LLM) which utilizes Chain of Thought (CoT) and a physics-based simulation engine for safely retrieving cluttered stacks of boxes on shelves, relationship graph for sub-task generation, extraction sequence planning and decision making. Furthermore, we validate the framework through real-world shelf picking experiments such as 1) Gesture-Guided Box Extraction, 2) Collaborative Shelf Clearing and 3) Collaborative Stability Assistance. |
| title | A Multi-Modal Interaction Framework for Efficient Human-Robot Collaborative Shelf Picking |
| topic | Robotics Human-Computer Interaction |
| url | https://arxiv.org/abs/2504.06593 |