Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
OTTER: A Vision-Language-Action Model with Text-Aware Visual Feature Extraction
Authors: Huang Huang, Fangchen Liu, Letian Fu, Tingfan Wu, Mustafa Mukadam, Jitendra Malik, Ken Goldberg, Pieter Abbeel
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In simulation and real-world experiments, OTTER significantly outperforms existing VLA models, demonstrating strong zero-shot generalization to novel objects and environments. Section 4: Experiments, further details the simulation and real-world experimental setup, baselines, and evaluation of OTTER's performance. |
| Researcher Affiliation | Collaboration | 1University of California, Berkeley 2Meta AI. Correspondence to: Huang Huang <EMAIL>, Fangchen Liu <fangchen EMAIL>, Letian Fu <EMAIL>. The authors are affiliated with both the University of California, Berkeley (an academic institution) and Meta AI (an industry research lab), indicating a collaborative effort. |
| Pseudocode | No | The paper describes the methods and model architecture in prose and mathematical equations (e.g., Section 3.1, Equation 1-4) and diagrams (Figure 2, 3), but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Video, code, checkpoints, and dataset: https://ottervla.github.io/. |
| Open Datasets | Yes | Video, code, checkpoints, and dataset: https://ottervla.github.io/. We use the LIBERO benchmark (Liu et al., 2024) for simulation evaluation. trained from scratch on 800K trajectories from the Open X-Embodiment dataset (Collaboration et al., 2024). |
| Dataset Splits | Yes | We use the LIBERO benchmark (Liu et al., 2024) for simulation evaluation. Specifically, we use the tasks and datasets in LIBERO-Spatial, LIBERO-Object, LIBERO-Goal, and LIBERO-90... Each simulation task has 50 demonstrations. We evaluate OTTER s capabilities on both in-distribution tasks and unseen tasks... We consider 19 in-distribution training tasks and 15 out-of-distribution unseen tasks across the 4 primitives (Table 6). |
| Hardware Specification | Yes | All the models are trained on 4 NVIDIA A100 80GB GPUs. This enables the Vi T-L/14 OTTER model to perform inference at 50Hz on a single NVIDIA 3090Ti, allowing real-time control. |
| Software Dependencies | No | The paper mentions using "CLIP" and refers to a "Vi T Encoder based on the implementation of https://github.com/google-research/vision_transformer" but does not provide specific version numbers for these or other key software libraries or frameworks (e.g., PyTorch, Python versions) used in the experiments. |
| Experiment Setup | Yes | Table 7: Hyperparameters for OTTER model architecture. Table 8: Hyperparameters used for training (pre-training on OXE). These tables provide specific values for numerous hyperparameters such as learning rate, batch size, context length, network dimensions, and image processing details. |