Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping
Authors: Tianhao Wu, Mingdong Wu, Jiyao Zhang, Yunchong Gan, Hao Dong
NeurIPS 2023 | Venue PDF | 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. |