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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Text-Aware Diffusion for Policy Learning
Authors: Calvin Luo, Mandy He, Zilai Zeng, Chen Sun
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we demonstrate that TADPo Le is able to learn policies for novel goal-achievement and continuous locomotion behaviors specified by natural language, in both Humanoid and Dog environments. The behaviors are learned zero-shot without ground-truth rewards or expert demonstrations, and are qualitatively more natural according to human evaluation. |
| Researcher Affiliation | Academia | Calvin Luo Mandy He Zilai Zeng Chen Sun Brown University EMAIL |
| Pseudocode | Yes | A pseudocode of the method is provided in Algorithm 1. |
| Open Source Code | Yes | Visualizations and code are provided at diffusion-supervision.github.io/tadpole/. |
| Open Datasets | Yes | We present our main results using the Dog and Humanoid environments from the Deep Mind Control Suite [39], and robotic manipulation tasks from Meta-World [42]. |
| Dataset Splits | No | The paper mentions training steps and evaluation rollouts but does not explicitly detail training, validation, and test dataset splits with percentages or sample counts. |
| Hardware Specification | Yes | All experiments are performed on a single NVIDIA V100 GPU. |
| Software Dependencies | No | The paper mentions software like Stable Diffusion 2.1, Animated Diff v2, TD-MPC, CLIP, and GPT-3.5 but does not provide specific version numbers for programming languages or libraries (e.g., Python, PyTorch, CUDA). |
| Experiment Setup | Yes | We use TD-MPC [14] as the reinforcement learning algorithm for all tasks... We train Humanoid and Dog agents for 2M steps, and Meta-World agents for 700K steps... We fix the reward weights w1 = 2000 and w2 = 200 based on Humanoid standing and walking performance, and study their impact in Appendix B.3. Selection of noise level is discussed in Appendix A. |