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 [1].
Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors
Authors: Karl Pertsch, Oleh Rybkin, Frederik Ebert, Shenghao Zhou, Dinesh Jayaraman, Chelsea Finn, Sergey Levine
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The aim of our experiments is to study the following questions: (1) Are the proposed GCPs able to effectively predict goal-directed trajectories in the image space and scale to long time horizons? (2) Is the proposed goal-conditioned hierarchical planning method able to solve long-horizon visual control tasks? (3) Does the version of GCP with adaptive binding ο¬nd high-level events in the trajectories? |
| Researcher Affiliation | Academia | Karl Pertsch ,1 Oleh Rybkin ,2 Frederik Ebert3 Chelsea Finn4 Dinesh Jayaraman2 Sergey Levine3 1 USC 2 UPenn 3 UC Berkeley 4 Stanford University |
| Pseudocode | Yes | Algorithm 1 Goal-Conditioned Hierarchical Planning |
| Open Source Code | Yes | Project page: orybkin.github.io/video-gcp |
| Open Datasets | Yes | We further evaluate on the real-world Human 3.6M video dataset [25] |
| Dataset Splits | No | The paper discusses training data and evaluation, but does not explicitly specify exact percentages or sample counts for training, validation, and test splits. It mentions collecting 'example trajectories' but does not detail the splits. |
| Hardware Specification | Yes | We use 16 16 px to ο¬t the 1000-frame sequences on a single NVIDIA V100 GPU |
| Software Dependencies | No | The paper refers to various methods and models, but does not provide specific version numbers for software dependencies or libraries used in their implementation (e.g., PyTorch, TensorFlow versions, or specific optimizer versions beyond Adam). |
| Experiment Setup | No | The paper mentions architectural details and hyperparameter considerations but defers specific details to the appendix: 'Architecture and hyperparameters are detailed in Appendix C.' Since the question asks for details *in the main text*, the answer is No for specific values. |