Lan-grasp: Using Large Language Models for Semantic Object Grasping (2310.05239v2)

Abstract: In this paper, we propose Lan-grasp, a novel approach towards more appropriate semantic grasping. We use foundation models to provide the robot with a deeper understanding of the objects, the right place to grasp an object, or even the parts to avoid. This allows our robot to grasp and utilize objects in a more meaningful and safe manner. We leverage the combination of a LLM, a Vision LLM, and a traditional grasp planner to generate grasps demonstrating a deeper semantic understanding of the objects. We first prompt the LLM about which object part is appropriate for grasping. Next, the Vision LLM identifies the corresponding part in the object image. Finally, we generate grasp proposals in the region proposed by the Vision LLM. Building on foundation models provides us with a zero-shot grasp method that can handle a wide range of objects without the need for further training or fine-tuning. We evaluated our method in real-world experiments on a custom object data set. We present the results of a survey that asks the participants to choose an object part appropriate for grasping. The results show that the grasps generated by our method are consistently ranked higher by the participants than those generated by a conventional grasping planner and a recent semantic grasping approach. In addition, we propose a Visual Chain-of-Thought feedback loop to assess grasp feasibility in complex scenarios. This mechanism enables dynamic reasoning and generates alternative grasp strategies when needed, ensuring safer and more effective grasping outcomes.

Semantic Object Grasping with LAN-grasp

The paper "LAN-grasp: Using LLMs for Semantic Object Grasping" by Mirjalili et al. presents an innovative approach to improving the semantic grasping capabilities of robots. The methodology leverages LLMs and Vision LLMs (VLMs) to enable robots to better understand objects and grasp them in a more context-appropriate manner. The proposed LAN-grasp method integrates foundation models with traditional grasp planners to accomplish zero-shot grasping without additional training or fine-tuning. This balanced combination offers a more meaningful and safe way for robots to interact with various objects.

Methodological Overview

The LAN-grasp approach is partitioned into two principal modules: the language module and the grasp planning module.

Language Module:

The language module uses a LLM to determine the suitable part of an object for grasping based on the name of the object given by the user. Specifically, the method employs GPT-4 for language processing. In response to user prompts, GPT-4 identifies which part of the object makes the most sense to grasp. Subsequently, a VLM (OWL-Vit in this case) grounds this understanding in the object image, marking the graspable part with a bounding box.

Grasp Planning Module:

The Grasp Planning Module utilizes GraspIt!, a well-established grasp planning simulator. The region identified by the VLM is integrated into a 3D reconstruction of the object. The grasp planner then generates multiple grasp proposals specifically within this targeted region. This selective grasping ensures tool usability and safety. An off-the-shelf grasp proposal tool helps to compute the optimal grasp poses, thereby integrating semantic insights into the mechanical operation of grasping.

Experimental Evaluation

The experimental evaluation was conducted on a diverse dataset of 22 household objects. Real-world experiments were executed using a Human Support Robot (HSR) to affirm the practical robustness of the method. For comparative analysis, the proposed LAN-grasp method was stacked against two baselines: (1) traditional grasp planning with GraspIt! and (2) the recent task-oriented grasping approach GraspGPT.

Numerical and User Study Results

The numerical results demonstrate that grasps generated by LAN-grasp are significantly preferred over those from the baselines. On average, LAN-grasp showed a similarity score of 0.94 compared to human-selected grasps, surpassing GraspGPT with 0.67 and GraspIt! with 0.31. These evaluations were derived from a survey involving 83 participants who indicated their preference for grasp locations on various objects.

Implications and Future Developments

Practical Implications:

The LAN-grasp method advances the state of robotic grasping by incorporating semantic understanding via foundation models. This improvement is vital for robots operating in human-centered environments, such as homes or hospitals, where the appropriate handling of objects ensures both efficiency and safety. By narrowing down grasp locations to contextually appropriate regions, robots can perform tasks more akin to human actions, thereby reducing the likelihood of object damage or misuse.

Theoretical Implications:

The paper underlines the potential of leveraging LLMs and VLMs in robotic applications, providing a template for future research that seeks to incorporate semantic and contextual comprehension into mechanical operations. The modular structure of LAN-grasp exemplifies the flexibility in integrating newer, more advanced models as they become available.

Speculations on Future Developments:

The current work opens avenues for further exploration in contextual task execution. Future research could extend this approach to not only focus on which parts to grasp but also integrate how to grasp and manipulate objects based on specific tasks. Enhancements could include the integration of newer versions of LLMs and VLMs, improved scene understanding, and more complex task execution frameworks.

Conclusion

Mirjalili et al.'s LAN-grasp marks a significant step towards semantically informed robotic grasping. By melding traditional grasp planning with advanced LLMs and VLMs, the research presents a practical, zero-shot solution for safe and meaningful robot-object interactions in everyday scenarios. This paper lays out a promising direction for the development of more intuitive and capable robotic systems, enhancing both the theoretical underpinnings and practical applications of semantic grasping.