Learning Active Camera for Multi-Object Navigation
Authors: Peihao Chen, Dongyu Ji, Kunyang Lin, Weiwen Hu, Wenbing Huang, Thomas Li, Mingkui Tan, Chuang Gan
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show our camera policy consistently improves the performance of multi-object navigation over four baselines on two datasets. |
| Researcher Affiliation | Collaboration | 1South China University of Technology, 2Pazhou Laboratory, 3MIT-IBM Watson AI Lab, 4UMass Amherst, 5Gaoling School of Artificial Intelligence, Renmin University of China, 6Information Technology R&D Innovation Center of Peking University, 7Key Laboratory of Big Data and Intelligent Robot, Ministry of Education |
| Pseudocode | Yes | Algorithm 1 Training method for active-camera agent. |
| Open Source Code | No | We will publish our code upon acceptance. |
| Open Datasets | Yes | We perform experiments on two photorealistic 3D indoor environments, i.e., Matterport3D [7] and Gibson [71]. |
| Dataset Splits | Yes | We have included the training details in the supplemental materials. |
| Hardware Specification | Yes | We have included the details of the computational resources in the supplemental material. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with version numbers. |
| Experiment Setup | Yes | For navigation and camera actions, a FORWARD action moves the agent forward by 0.25 meters and a TURN action turns by 30 . The maximum episode time step is 500... We set K 8, r 2.4, γ rˆ1.2 2.88 in heuristic module empirically... Reward scaling factors α and β are set to 10 and 1 respectively such that three reward terms are in the same order of magnitude at initialization. |