Compositional Foundation Models for Hierarchical Planning
Authors: Anurag Ajay, Seungwook Han, Yilun Du, Shuang Li, Abhi Gupta, Tommi Jaakkola, Josh Tenenbaum, Leslie Kaelbling, Akash Srivastava, Pulkit Agrawal
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the efficacy and adaptability of our approach in three different long-horizon table-top manipulation tasks. and 3 Experimental Evaluations |
| Researcher Affiliation | Collaboration | Improbable AI Lab MIT-IBM Watson AI Lab Massachusetts Institute Technology https://hierarchical-planning-foundation-model.github.io/ Anurag Ajay , Seungwook Han * Yilun Du * , Shuang Li , Abhi Gupta , Tommi Jaakkola , Josh Tenenbaum , Leslie Kaelbling , Akash Srivastava , Pulkit Agrawal and Correspondence to aajay@mit.edu, swhan@mit.edu and yilundu@mit.edu. |
| Pseudocode | Yes | Algorithm 1 Decision Making with Hi P |
| Open Source Code | Yes | https://hierarchical-planning-foundation-model.github.io/ |
| Open Datasets | Yes | We pretrain it pϕ(τ i x|wi, xi,1) on a large-scale text-to-video dataset Ego4D [13]. |
| Dataset Splits | No | The paper defines `Ttrain` and `Ttest` for generating datasets and evaluating performance, but does not explicitly describe a separate validation dataset split with percentages, counts, or specific methodology for reproduction. |
| Hardware Specification | Yes | We used one V100 Nvidia GPU for training the multi-class classifier. and We used two A6000 Nvidia GPUs for training these diffusion models. |
| Software Dependencies | No | The paper refers to specific models (e.g., Flan-T5-Base, GPT3.5-turbo) and codebases (e.g., PVDM, Vima, Say Can) but does not provide specific version numbers for underlying software dependencies like Python or PyTorch. |
| Experiment Setup | Yes | Task Planning We train fϕ for 50 epochs using Adam W optimizer [31], a batch size of 256, a learning rate of 1e 3 and a weight decay of 1e 6. and Visual Planning ... We use Adam W optimizer [31], a batch size of 24 and a learning rate of 1e 4 for training the autoencoder. and Action Planning We train VC-1 initialized inverse dynamics model for 20 epochs with Adam W optimizer [31], a batch size of 256 and a learning rate of 3e 5. |