LY-GWM-RoboCasa-Human300: Prediction-Guided Action Selection with a Graph Dynamics Model
We present LY-GWM-RoboCasa-Human300, a policy that combines a Human300-fine-tuned GR00T-N1.5 action generator, an action-conditioned graph dynamics model, and a learned binary state-reward scorer.
ProofPaper ↗
Key points
- At inference, the policy samples four action chunks, predicts the future latent features and robot state for each, and executes the candidate with the highest predicted-state reward logit.
- Predictions therefore influence the actions sent to the environment.
- In the local evaluation on 50 RoboCasa 1.0.1 tasks in the pretrain kitchen split, with 50 episodes per task, the system records 207/2,500 successes (8.28%), compared with 181/2,500 (7.24%) in the historical self-trained GR00T-N1.5 baseline.
- We describe the actual training stages, implemented inference mechanism, evaluation provenance, and public reproduction entrypoints.
Sources (1)
- [1]LY-GWM-RoboCasa-Human300: Prediction-Guided Action Selection with a Graph Dynamics ModelarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 10:44 AM
We present LY-GWM-RoboCasa-Human300, a policy that combines a Human300-fine-tuned GR00T-N1.5 action generator, an action-conditioned graph dynamics model, and a learned binary state-reward scorer.
At inference, the policy samples four action chunks, predicts the future latent features and robot state for each, and executes the candidate with the highest predicted-state reward logit.
Extractive summary: sentences quoted from the sources.