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..
Model-Based Visual Planning with Self-Supervised Functional Distances
Authors: Stephen Tian, Suraj Nair, Frederik Ebert, Sudeep Dasari, Benjamin Eysenbach, Chelsea Finn, Sergey Levine
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we find that our method can successfully learn models that perform a variety of tasks at test-time, moving objects amid distractors with a simulated robotic arm and even learning to open and close a drawer using a real-world robot. In comparisons, we find that this approach substantially outperforms both model-free and model-based prior methods. Videos and visualizations are available here: https://sites.google.com/berkeley.edu/mbold. |
| Researcher Affiliation | Academia | 1University of California, Berkeley 2Stanford University 3Carnegie Mellon University |
| Pseudocode | No | The paper describes the Model Predictive Control (MPC) algorithm and its components (like CEM) in text, but it does not provide a formally labeled “Pseudocode” or “Algorithm” block with structured steps. |
| Open Source Code | No | The paper states: “Videos and visualizations are available here: https://sites.google.com/berkeley.edu/mbold.” and “Videos of both simulated and real-world task execution can be found at the project website: https://sites.google.com/berkeley.edu/mbold.” These links are for videos and visualizations/project website, not explicit code repositories. |
| Open Datasets | Yes | The Sawyer environments are adapted from the Meta-World benchmark (Yu et al., 2019a), and the door sliding environment is based off of the environment presented by Lynch et al. (2020). ... We additionally evaluate MBOLD in a real-world drawer manipulation task using a 7-Do F Franka arm. We train the dynamics model and distance function on a preexisting dataset of 1000 trajectories collected by a weakly supervised batch exploration algorithm in prior work (Chen et al., 2020). |
| Dataset Splits | Yes | Table 2: Hyperparameters for distance learning: Train/test/val split 0.9/0.05/0.05 |
| Hardware Specification | No | This research used the Savio computational cluster resource provided by the Berkeley Research Computing program at the University of California, Berkeley. |
| Software Dependencies | No | We build off of the open source implementation of Dreamer by the original authors, written in Tensor Flow2... We use the open-source implementation of soft actor-critic (SAC) in RLKit... We implement the k-NN search using the GPU-enabled FAISS library (Johnson et al., 2017). |
| Experiment Setup | Yes | Table 2: Hyperparameters for distance learning. Table 3: Hyperparameters for model-based planning. ... Additional training hyperparameters are detailed in Table 2. In Table 3, we describe the parameters for model-based planning in our experiments. ... The particular choice of model is a design decision when implementing our method. In our implementation, we use a convolutional video prediction model adapted from SAVP (Lee et al., 2018). |