Hannibal52Barca2026MIT
ICL Project — Value/Reward Model Evaluation Results
Per-frame progress predictions and evaluation outputs for seven value/reward models (IC-VFE, RECAP, Robometer, Robo-Dopamine, TOPReward, GVL, SARM) compared in the VICTR paper on the ICL demo dataset.
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Why This Matters for Physical AI
Provides standardized evaluation benchmarks for value and reward prediction models across multiple state-of-the-art approaches, essential for advancing interpretable progress estimation in robotic learning systems.
Technical Profile
- Modalities
- rgb
- Task Types
- value_estimationreward_estimation
- Data Format
- zip archives
- Annotation Types
- reward_labelsprogress_predictions
- License
- MIT
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