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..
Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation
Authors: Cansu Sancaktar, Sebastian Blaes, Georg Martius
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our empirical evaluation, we analyze the performance of CEE-US on two different object manipulation environments to answer the questions: How much does the structural inductive bias introduced by GNNs help model learning and control? Does the free-play phase create rich interaction data that helps downstream task performance? Can we solve challenging manipulation tasks in a zero-shot manner? |
| Researcher Affiliation | Academia | Cansu Sancaktar Sebastian Blaes Georg Martius Max Planck Institute for Intelligent Systems Tübingen, Germany EMAIL |
| Pseudocode | Yes | Algorithm 1 CEE-US: Free Play in Intrinsic Phase, Algorithm 2 CEE-US: Zero-shot Generalization in Extrinsic Phase |
| Open Source Code | Yes | Code and videos are available at https://martius-lab.github.io/cee-us. |
| Open Datasets | Yes | Fetch Pick & Place Construction This is an extension of the Fetch Pick & Place environment [23] to more cubes [24] (Fig. 2). |
| Dataset Splits | No | The paper mentions "collected rollouts are added to the buffer" and "fixed dataset" in the context of training, and refers to "500k datapoints" and "600k datapoints" in Table 3 but does not specify explicit training/validation/test dataset splits with percentages or counts. |
| Hardware Specification | No | The paper states "Further details are in the Suppl. C" regarding compute, but the provided text does not include the content of Suppl. C, and no specific hardware models (GPU/CPU) are mentioned in the main body. |
| Software Dependencies | No | The paper mentions using "i CEM", "DDPG", "CQL", and the "d3rlpy library" but does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | No | The paper mentions the ensemble size (M=5) and the use of specific algorithms like i CEM, DDPG, and CQL, but refers to "Suppl. C" and "Suppl. C.5" for training details and hyperparameters, which are not provided in the main text. |