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..
MyoDex: A Generalizable Prior for Dexterous Manipulation
Authors: Vittorio Caggiano, Sudeep Dasari, Vikash Kumar
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate Myo Dex s effectiveness in fewshot generalization as well as positive transfer to a large repertoire of unseen dexterous manipulation tasks. Myo Dex can solve approximately 3x more tasks and it can accelerate the achievement of solutions by about 4x in comparison to a distillation baseline. |
| Researcher Affiliation | Collaboration | 1FAIR, Meta AI 2CMU. Correspondence to: Vittorio Caggiano <EMAIL>, Sudeep Dasari <EMAIL>, Vikash Kumar <EMAIL>. |
| Pseudocode | No | The paper describes the reinforcement learning setup but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions a project webpage (https://sites.google.com/view/myodex) but does not contain an explicit statement about releasing source code or a direct link to a code repository within its text. |
| Open Datasets | Yes | Every task setup (see Figure 3) consists of a tabletop environment, an object from the Contact DB dataset (Brahmbhatt et al.), and the Myo Hand. |
| Dataset Splits | No | The paper describes using a subset of 14 Myo DM tasks for pre-training and 43 'out-of-domain' tasks for fine-tuning/evaluation, but it does not specify explicit train/validation/test dataset splits with percentages or sample counts for data within each task. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions software like MuJoCo, Stable-Baselines, and scikit-learn, but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | Table A.1: Parameters adopted for the reinforcement learning models. Samples for Iterations 4096 Discount Factor (γ) 0.95 GAE-λ 0.95 VF Coefficient (c1) 0.5 Entropy Bonus (c2) 0.001 Clip Parameter (ϵ) 0.2 Batch Size 256 Epochs 5 Network Size pi = [256, 128], vf = [256, 128] |