PhyGrasp: Generalizing Robotic Grasping with Physics-informed Large Multimodal Models
<|reference_start|>PhyGrasp: Generalizing Robotic Grasping with Physics-informed Large Multimodal Models: Robotic grasping is a fundamental aspect of robot functionality, defining how robots interact with objects. Despite substantial progress, its generalizability to counter-intuitive or long-tailed scenarios, such as objects with uncommon materials or shapes, remains a challenge. In contrast, humans can easily apply their intuitive physics to grasp skillfully and change grasps efficiently, even for objects they have never seen before.   This work delves into infusing such physical commonsense reasoning into robotic manipulation. We introduce PhyGrasp, a multimodal large model that leverages inputs from two modalities: natural language and 3D point clouds, seamlessly integrated through a bridge module. The language modality exhibits robust reasoning capabilities concerning the impacts of diverse physical properties on grasping, while the 3D modality comprehends object shapes and parts. With these two capabilities, PhyGrasp is able to accurately assess the physical properties of object parts and determine optimal grasping poses. Additionally, the model's language comprehension enables human instruction interpretation, generating grasping poses that align with human preferences. To train PhyGrasp, we construct a dataset PhyPartNet with 195K object instances with varying physical properties and human preferences, alongside their corresponding language descriptions. Extensive experiments conducted in the simulation and on the real robots demonstrate that PhyGrasp achieves state-of-the-art performance, particularly in long-tailed cases, e.g., about 10% improvement in success rate over GraspNet. Project page: https://sites.google.com/view/phygrasp<|reference_end|>
@article{guo2024phygrasp:,
    title={PhyGrasp: Generalizing Robotic Grasping with Physics-informed Large
  Multimodal Models},
    author={Dingkun Guo, Yuqi Xiang, Shuqi Zhao, Xinghao Zhu, Masayoshi Tomizuka,
  Mingyu Ding, Wei Zhan},
    journal={arXiv preprint arXiv:2402.16836},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16836},
    primaryClass={cs.RO cs.AI cs.CL cs.CV}
}