Conformal Prediction for Uncertainty-Aware Planning with Diffusion Dynamics Model
Authors: Jiankai Sun, Yiqi Jiang, Jianing Qiu, Parth Nobel, Mykel J Kochenderfer, Mac Schwager
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our algorithm on various planning tasks and model-based offline reinforcement learning tasks and show that it reduces the uncertainty of the learned trajectory prediction model. |
| Researcher Affiliation | Academia | Jiankai Sun Stanford University jksun@stanford.edu Yiqi Jiang Stanford University yqjiang@stanford.edu Jianing Qiu Imperial College London jianing.qiu17@imperial.ac.uk Parth Talpur Nobel Stanford University ptnobel@stanford.edu Mykel Kochenderfer Stanford University mykel@stanford.edu Mac Schwager Stanford University schwager@stanford.edu |
| Pseudocode | Yes | Algorithm 1: Plan CP: Conformal Prediction for Planning with Diffusion Dynamics Models |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of its source code. |
| Open Datasets | Yes | Finally, we evaluate the ability to recover an effective single-task controller from heterogeneous data of varying quality using the D4RL Benchmark [75]. |
| Dataset Splits | Yes | We have split the dataset D into three parts Dtrain, Dcal, and Dtest for training, calibration, and testing, respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as CPU, GPU models, or memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using the Adam optimizer but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We set the uncertainty weight to λuncertainty = 5 and the failure probability to α = 0.1. To optimize the model, we use the Adam [73, 74] optimizer with a learning rate of 2 10 4. We train the diffusion dynamics model on the training set Dtrain for 2 105 iterations. |