Zero-Shot Visual Imitation
Authors: Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A. Efros, Trevor Darrell
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our zero-shot imitator in two real-world settings: complex rope manipulation with a Baxter robot and navigation in previously unseen office environments with a Turtle Bot. Through further experiments in Viz Doom simulation, we provide evidence that better mechanisms for exploration lead to learning a more capable policy which in turn improves end task performance. Videos, models, and more details are available at https://pathak22.github.io/zeroshot-imitation/. |
| Researcher Affiliation | Academia | UC Berkeley {pathak,parsa.m,michaelluo,pulkitag,dianchen,fredshentu,shelhamer,malik,efros,trevor}@cs.berkeley.edu |
| Pseudocode | No | No pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | Videos, models, and more details are available at https://pathak22.github.io/zeroshot-imitation/. |
| Open Datasets | No | No specific link, DOI, repository name, or formal citation with authors/year was provided for publicly available or open datasets. The paper mentions using data from Nair et al. (2017) and collecting their own data but no clear access information. |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) was found. |
| Hardware Specification | No | The paper mentions a "Baxter robot" and a "Turtle Bot2 robot comprising of a wheeled Kobuki base and an Orbbec Astra camera" and a "powerful on-board laptop". However, no specific GPU/CPU models, processor types, or memory details are provided for the compute used for training or inference of the models. |
| Software Dependencies | No | The paper mentions "Adam (Kingma & Ba, 2015)" as an optimizer, and "Alex Net" and "Res Net-50 (He et al., 2016)" as architectures, but no specific software library names with version numbers (e.g., PyTorch 1.9) were found for reproducibility. |
| Experiment Setup | Yes | The loss weight of the forward model is 0.1, and the objective is minimized using Adam (Kingma & Ba, 2015) with learning rate of 5e 4. All models were trained with batch size 64, Adam Solver with 1e-4 learning rate, and landmark slices uniformly sampled between 5 to 15 action steps for each batch. The observations are 42x42 resolution, grayscale images with only one-time channel both for goal and current state. |