Multiple Thinking Achieving Meta-Ability Decoupling for Object Navigation
Authors: Ronghao Dang, Lu Chen, Liuyi Wang, Zongtao He, Chengju Liu, Qijun Chen
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments on AI2-Thor and Robo THOR, we demonstrate that our method outperforms stateof-the-art (SOTA) methods on both typical and zero-shot object navigation tasks. |
| Researcher Affiliation | Academia | 1Department of Control Science and Engineering, Tongji University, Shanghai 201804, China. Correspondence to: Chengju Liu <liuchengju@tongji.edu.cn>. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | Datasets AI2-Thor (Kolve et al., 2017) and Robo THOR (Deitke et al., 2020) are our primary experimental platforms. AI2-Thor includes 30 different floorplans for each of 4 room layouts: kitchen, living room, bedroom, and bathroom. For each scene type, we use 20 rooms for training, 5 rooms for validation, and 5 rooms for testing. Robo THOR consists of a set of 89 apartments, 75 of which are accessible. we use 60 for training and 15 for validation. |
| Dataset Splits | Yes | For each scene type, we use 20 rooms for training, 5 rooms for validation, and 5 rooms for testing. Robo THOR consists of a set of 89 apartments, 75 of which are accessible. we use 60 for training and 15 for validation. |
| Hardware Specification | Yes | We train our model with 18 workers on 2 RTX 2080Ti Nvidia GPUs, in a total of 3M navigation episodes. |
| Software Dependencies | No | The paper mentions using the asynchronous advantage actor-critic (A3C) algorithm, ResNet18, DETR, and LSTM, but does not specify exact version numbers for programming languages, libraries, or frameworks (e.g., Python 3.x, PyTorch 1.x, CUDA 11.x). |
| Experiment Setup | Yes | The dropout rate is set to 0.3, and the meta-ability reward RMA is only utilized in the first 0.2M (C) episodes. |