Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation
Authors: Cansu Sancaktar, Sebastian Blaes, Georg Martius
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 {cansu.sancaktar, sebastian.blaes, georg.martius}@tue.mpg.de |
| 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. |