amazon-far / PRISM

Counterfactual Video Generation Enables Scalable Humanoid Loco-Manipulation
 This repository provides simulation training and real-robot deployment for PRISM, based on HoloSoma .
## Code branches
| Branch | Use |
|---|---|
| `main` | Simulation: teacher training, rollout collection and student distillation |
| `sim2real` | Real-robot deployment with FastFoundationStereo |
For real-robot deployment, switch an existing clone to `sim2real` and follow that branch's README:
 [code] 
## Installation
Linux, Python 3.11 and a compatible NVIDIA GPU are required.
See system requirements and environment details.
 [code] 
## Data
Download and prepare the Hugging Face dataset under `data/`:
 [code] 
Includes object meshes and prepares the student training shards.
Dataset details.
Both training scripts use all visible GPUs on this machine. Set
`CUDA_VISIBLE_DEVICES=0` for one GPU or `CUDA_VISIBLE_DEVICES=0,1` for a subset.
Use `--envs-per-gpu` to lower memory use. Training details.
## Teacher Training
Train a privileged motion-tracking policy with PPO.
Teacher-training data is still under review; supply a prepared teacher bank.
 [code] 
## Rollout
Collect teacher trajectories and contact sidecars into `outputs/rollout/`.
 [code] 
## Student Distillation
Distill the teacher into a depth policy using the prepared data under `data/`.
 [code] 
## Citation
If you use PRISM in your research, please cite:
 [code] 
## License and security
See LICENSE, NOTICE and THIRD_PARTY_LICENSES.
Report security issues through AWS Vulnerability Reporting.