MoVie: Visual Model-Based Policy Adaptation for View Generalization
Authors: Sizhe Yang, Yanjie Ze, Huazhe Xu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform extensive experiments on 7 robotic manipulation tasks (Adroit hand [20] and x Arm [7]) and 11 locomotion tasks (DMControl suite [30])), across the proposed 4 view generalization settings, totaling 18 4 configurations. In this section, we investigate how well an agent trained on a single fixed view generalizes to unseen views during test time. |
| Researcher Affiliation | Collaboration | Sizhe Yang12 , Yanjie Ze13 , Huazhe Xu415 1Shanghai Qi Zhi Institute, 2University of Electronic Science and Technology of China, 3Shanghai Jiao Tong University, 4Tsinghua University, 5Shanghai AI Lab Equal contribution |
| Pseudocode | No | No dedicated 'Pseudocode' or 'Algorithm' block was found. The paper provides network architecture descriptions in code-like format in Appendix A, but not a full algorithm for the overall method. |
| Open Source Code | Yes | Code and videos are available at yangsizhe.github.io/Mo Vie. |
| Open Datasets | Yes | Our test platform consists of 18 tasks from 3 domains: 11 tasks from DMControl [30], 3 tasks from Adroit [20], and 4 tasks from x Arm [7]. |
| Dataset Splits | No | The paper describes training and testing phases and data collection during evaluation, but does not specify explicit training/validation/test dataset splits with percentages or counts, which is common in supervised learning. |
| Hardware Specification | Yes | One seed of our experiments could be run on a single 3090 GPU with fewer than 2GB and it takes 1 hours for test-time training. |
| Software Dependencies | No | The paper mentions using 'official implementation of TD-MPC [11] and Mo Dem [8]' but does not specify version numbers for general software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | We present relevant hyperparameters during both training and test time in Table 10 and Table 11. |