D-RMGPT: Robot-assisted collaborative tasks driven by large multimodal models (2408.11761v1)

Abstract: Collaborative robots are increasingly popular for assisting humans at work and daily tasks. However, designing and setting up interfaces for human-robot collaboration is challenging, requiring the integration of multiple components, from perception and robot task control to the hardware itself. Frequently, this leads to highly customized solutions that rely on large amounts of costly training data, diverging from the ideal of flexible and general interfaces that empower robots to perceive and adapt to unstructured environments where they can naturally collaborate with humans. To overcome these challenges, this paper presents the Detection-Robot Management GPT (D-RMGPT), a robot-assisted assembly planner based on Large Multimodal Models (LMM). This system can assist inexperienced operators in assembly tasks without requiring any markers or previous training. D-RMGPT is composed of DetGPT-V and R-ManGPT. DetGPT-V, based on GPT-4V(vision), perceives the surrounding environment through one-shot analysis of prompted images of the current assembly stage and the list of components to be assembled. It identifies which components have already been assembled by analysing their features and assembly requirements. R-ManGPT, based on GPT-4, plans the next component to be assembled and generates the robot's discrete actions to deliver it to the human co-worker. Experimental tests on assembling a toy aircraft demonstrated that D-RMGPT is flexible and intuitive to use, achieving an assembly success rate of 83% while reducing the assembly time for inexperienced operators by 33% compared to the manual process. http://robotics-and-ai.github.io/LMMmodels/

• The paper introduces D-RMGPT, a novel framework that combines multimodal detection and robot management to enhance assembly efficiency by up to 33%.
• It leverages GPT-4V for one-shot image analysis and GPT-4 for planning robot actions, achieving an 83% success rate in experimental trials.
• The approach simplifies onboarding for novice operators and demonstrates robust adaptation in dynamic assembly scenarios without extensive prior training.

Overview of D-RMGPT: Robot-assisted Collaborative Tasks Driven by Large Multimodal Models

The paper "D-RMGPT: Robot-assisted collaborative tasks driven by large multimodal models" presents a novel approach to facilitating human-robot interaction (HRI) for assembly tasks. This approach leverages Large Multimodal Models (LMM) to create a flexible and adaptive assembly planner, termed Detection-Robot Management GPT (D-RMGPT). This system aims to assist inexperienced operators effectively in assembly processes without prior training or markers.

Architecture and Components

D-RMGPT consists of two critical modules:

1. DetGPT-V (Detection GPT based on GPT-4V):
   • Performs one-shot analysis of images to identify the assembly status.
   • Detects which components have already been assembled using images of the assembly stage and a components list.
   • Operates without the requirement for extensive datasets or markers.
2. R-ManGPT (Robot Management based on GPT-4):
   • Plans the next assembly step, suggesting components based on the current status provided by DetGPT-V.
   • Generates discrete robot actions to deliver components to the human co-worker.

Experimental Results

The effectiveness of D-RMGPT was demonstrated through a series of experiments with an assembly task involving a toy aircraft. Key findings include:

• Success Rate: The system achieved an 83% success rate during the experimental setups.
• Efficiency Improvement: For inexperienced operators, D-RMGPT reduced assembly time by approximately 33% compared to manual assembly.
• Accuracy: DetGPT-V demonstrated superior performance in component detection over existing VLM-based detectors, such as ViLD and OW-ViT, particularly in distinguishing similar-looking components.

Detailed Tests

1. Inexperienced Operators:
   • Twelve different operators performed the assembly task with D-RMGPT’s guidance. In ten out of twelve cases, the assembly was completed successfully.
   • The system showed resilience against minor detection inaccuracies (false positives and false negatives).
2. Experienced Operators:
   • Evaluated the system's flexibility when the operator did not follow the recommended sequence. D-RMGPT adapted successfully, demonstrating its robustness.
3. Comparison with Manual Assembly:
   • A comparative analysis between D-RMGPT-assisted and manual assembly was conducted. Operators using D-RMGPT completed the task faster and with less variability, underscoring the system's capability to streamline and standardize the assembly process.

Implications and Future Directions

The research underlines the potential of LMMs in transforming HRI by enabling more intuitive and adaptable interfaces. Practically, D-RMGPT can facilitate widespread adoption in industrial settings where flexible, real-time human-robot collaboration is necessary. Theoretically, it validates the capability of foundation models to perform complex, real-world tasks in dynamic environments.

Future work will focus on:

• Enhancing detection accuracy by incorporating additional image perspectives while managing processing times.
• Integrating features for learning operator preferences, thereby increasing system intuitiveness and user-friendliness.

The paper's contribution is significant in demonstrating a scalable and reliable approach to collaborative robotics, leveraging the synergies between advanced perception, reasoning capabilities of LMMs, and real-world robotic actions. This paradigm not only simplifies the onboarding process for novice operators but also illustrates how generalizable AI models can be employed to handle diverse and uncertain environments efficiently.