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 | Venue 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 EMAIL Yiqi Jiang Stanford University EMAIL Jianing Qiu Imperial College London EMAIL Parth Talpur Nobel Stanford University EMAIL Mykel Kochenderfer Stanford University EMAIL Mac Schwager Stanford University EMAIL |
| 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. |