Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping
Authors: Tianhao Wu, Mingdong Wu, Jiyao Zhang, Yunchong Gan, Hao Dong
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the superiority of our proposed method compared to baselines, highlighting user awareness and practicality in real-world applications.In our experiments, we evaluate several methods on a dexterous grasping environment that assists humans in grasping over 4900+ on-table objects with up to 200 realistic human wrist movement patterns. |
| Researcher Affiliation | Academia | Tianhao Wu 1,2,3*, Mingdong Wu 1,3*, Jiyao Zhang1,2,3, Yunchong Gan1, Hao Dong1,3 1 Center on Frontiers of Computing Studies, School of Computer Science, Peking University 2 Beijing Academy of Artificial Intelligence 3 National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University |
| Pseudocode | No | The paper describes the methods in prose and mathematical formulations but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The codes and demonstrations can be viewed at https://sites.google.com/view/graspgf. |
| Open Datasets | Yes | We created our success grasping pose based on the Uni Dex Grasp dataset [10].To mimic real human grasping patterns, we resampled 200 real human grasping wrist trajectories from Handover Sim! [28] |
| Dataset Splits | Yes | The dataset was split into three sets: training instances (3127 objects, 363,479 grasps), seen category unseen instances (519 objects, 2595 grasps), and unseen category instances (1298 objects, 6490 grasps). |
| Hardware Specification | Yes | It takes 60 hours to train on a single A100 for primitive policy to converge.We trained the residual policy for a total of 10 million agent steps, which took approximately 15 hours using a single A100 GPU.We evaluate the inference speed on the GTX 1650, which is also used in our real-world experiment. |
| Software Dependencies | No | The paper mentions "Py Torch implementation" but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | we set the number of update intervals (nsteps) to 50, the number of optimization epochs (noptepochs) to 2, the mini-batch size mini_batch_size(mini_batch_size) to 64, and the discount factor (gamma) to 0.99.Empirically, we set λs = 1.0, λa = 0.09, and λh = 0.5 for our approach. |