RoboCLIP: One Demonstration is Enough to Learn Robot Policies
Authors: Sumedh Sontakke, Jesse Zhang, Séb Arnold, Karl Pertsch, Erdem Bıyık, Dorsa Sadigh, Chelsea Finn, Laurent Itti
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Robo CLIP on the Metaworld Environment suite [Yu et al., 2020] and on the Franka Kitchen Environment [Gupta et al., 2019], and find that policies obtained by pretraining on the Robo CLIP reward result in 2 3 higher zero-shot task success in comparison to state-of-the-art imitation learning baselines. and 4 Experiments We test out each of the hypotheses defined in Section 1 on simulated robotic environments. |
| Researcher Affiliation | Collaboration | 1Thomas Lord Department of Computer Science, University of Southern California 2University of California, Berkeley 3Stanford University 4Google Research |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper mentions 'Visit our website for experiment videos.' but does not state that the source code for the methodology is available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We evaluate Robo CLIP on the Metaworld Environment suite [Yu et al., 2020] and on the Franka Kitchen Environment [Gupta et al., 2019] and The backbone model used in Robo CLIP is S3D [Xie et al., 2018] trained on the Howto100M dataset [Miech et al., 2019]. |
| Dataset Splits | No | The paper describes pretraining and finetuning phases, and mentions zero-shot evaluation, but does not specify explicit train, validation, or test dataset splits with percentages or sample counts for its experiments. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., GPU, CPU models, or cloud computing instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions using PPO [Schulman et al., 2017] but does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | No | The paper states that agents are trained with PPO but does not provide specific experimental setup details such as hyperparameter values, learning rates, batch sizes, or network configurations. |