MultiON: Benchmarking Semantic Map Memory using Multi-Object Navigation
Authors: Saim Wani, Shivansh Patel, Unnat Jain, Angel Chang, Manolis Savva
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform a set of multi ON experiments to examine how a variety of agent models perform across a spectrum of navigation task complexities. Our experiments show that: |
| Researcher Affiliation | Academia | 1IIT Kanpur 2UIUC 3Simon Fraser University |
| Pseudocode | No | The paper provides architectural diagrams (Figure 1 and Figure 2) and describes the agent models and training process in text, but it does not include any formally presented pseudocode or algorithm blocks. |
| Open Source Code | Yes | https://shivanshpatel35.github.io/multi-ON/ (This link leads to a project page which then provides a direct GitHub link to the code: https://github.com/shivanshpatel35/multi-ON) |
| Open Datasets | Yes | We generate datasets for the 1-ON, 2-ON, and 3-ON tasks on the Matterport3D [11] scenes using the standard train/val/test split. |
| Dataset Splits | Yes | For each m-ON dataset the train split consists of 50,000 episodes per scene, and the validation and test splits each contain 12,500 episodes per scene. |
| Hardware Specification | No | The paper mentions training with '16 parallel threads' and indicates support from 'West Grid' and 'Compute Canada', which are high-performance computing organizations. However, it does not provide specific hardware details such as CPU/GPU models, memory, or cloud instance types used for the experiments. |
| Software Dependencies | No | The paper mentions the use of 'proximal policy optimization (PPO)' as a training algorithm but does not list any specific software dependencies or libraries with version numbers (e.g., Python, PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | The agent is trained using the reward structure defined above using proximal policy optimization (PPO) [40]. All the agent models are trained for 40 million frames using 16 parallel threads. We use 4 mini-batches and do 2 epochs in each PPO update. We use rgoal = 3.0 and α = 0.01 for all experiments. The maximum number of steps is 2,500. |