RealDex: Towards Human-like Grasping for Robotic Dexterous Hand
Authors: Yumeng Liu, Yaxun Yang, Youzhuo Wang, Xiaofei Wu, Jiamin Wang, Yichen Yao, Sören Schwertfeger, Sibei Yang, Wenping Wang, Jingyi Yu, Xuming He, Yuexin Ma
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments have demonstrated the superior performance of our method on Real Dex and other open datasets. |
| Researcher Affiliation | Academia | 1Shanghai Tech University, 2The University of Hong Kong, 3Texas A&M University |
| Pseudocode | No | The paper describes methods in text and equations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The dataset and associated code are available at https://4dvlab.github.io/Real Dex page/. |
| Open Datasets | Yes | The dataset and associated code are available at https://4dvlab.github.io/Real Dex page/. ... We also conduct evaluation on human hand grasping dataset GRAB [Taheri et al., 2020]. ... We evaluate on GRAB [Taheri et al., 2020], Dex Grasp Net [Wang et al., 2023] and our dataset Real Dex. |
| Dataset Splits | Yes | We have divided our dataset into training, validation, and test sets, ensuring that each object appears exclusively in one of these subsets. The three sets contain 2114 grasping motion sequences for 40 objects, 245 grasping motions for 6 objects, and 271 grasping motions for 6 objects, respectively. |
| Hardware Specification | Yes | Models were both trained and tested on an Ubuntu server, equipped with eight NVIDIA Ge Force RTX 3090 GPU cards. |
| Software Dependencies | No | The paper mentions 'Python with Py Torch framework' and 'Gemini [Google, 2023]' but does not provide specific version numbers for these software components. |
| Experiment Setup | No | More details for the training process, inference process, loss functions are introduced in the supplementary material. |