Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Visual Adversarial Imitation Learning using Variational Models
Authors: Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 17,036 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,450 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experiments involving several vision-based locomotion and manipulation tasks, we find that V-MAIL learns successful visuomotor policies in a sample-efficient manner, has better stability compared to prior work, and also achieves higher asymptotic performance. |
| Researcher Affiliation | Collaboration | Rafael Rafailov1 Tianhe Yu1 Aravind Rajeswaran2,3 Chelsea Finn1 EMAIL, EMAIL 1 Stanford University, 2 University of Washington, 3 Facebook AI Research |
| Pseudocode | Yes | Algorithm 1 V-MAIL: Variational Model-Based Adversarial Imitation Learning |
| Open Source Code | No | All results including videos can be found online at https://sites.google.com/view/variational-mail. |
| Open Datasets | Yes | These consist of two locomotion environments from the Deep Mind Control Suite [30], the classic Car Racing environment from Open AI Gym [31] and two dexterous manipulation tasks using the D Claw [32] and Shadow Hand platforms. |
| Dataset Splits | No | The agent is provided with a fixed set of expert demonstrations collected by executing an expert policy πE, which we assume is optimal under the unknown reward function. |
| Hardware Specification | Yes | All experiments were carried out on a single Titan RTX GPU using an internal cluster for about 1000 GPU hours. |
| Software Dependencies | No | For the former, we choose DAC [18] as a representative approach, which we equip with Dr Q data augmentation for greater performance on vision-based tasks. |
| Experiment Setup | No | For implementation details, see the appendix. |