RoboAlign-R1: Distilled Multimodal Reward Alignment for Robot Video World Models
cs.RO, cs.AI
Submitted: 2026-05-05
Updated: 2026-09-27
Code: https://github.com/Alexander-wu/RoboAlign_R1
Terminology
Sources
- Qwen2.5-VL Technical Report
- Dream to Manipulate: Compositional World Models Empowering Robot Imitation Learning with Imagination
- RT-1: Robotics Transformer for Real-World Control at Scale
- World Models
- TD-MPC2: Scalable, Robust World Models for Continuous Control
- Vid2World: Crafting Video Diffusion Models to Interactive World Models
- WorldModelBench: Judging Video Generation Models As World Models
- Evaluating Real-World Robot Manipulation Policies in Simulation
- Robot Learning from a Physical World Model
- Deep multi-scale video prediction beyond mean square error
- Video Diffusion Alignment via Reward Gradients
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- BridgeData V2: A Dataset for Robot Learning at Scale
- BeamVQ: Beam Search with Vector Quantization to Mitigate Data Scarcity in Physical Spatiotemporal Forecasting
- BeamVQ: Aligning Space-Time Forecasting Model via Self-training on Physics-aware Metrics
- RLVR-World: Training World Models with Reinforcement Learning
- Efficient Streaming Language Models with Attention Sinks
- PhyCritic: Multimodal Critic Models for Physical AI
- VideoGPT: Video Generation using VQ-VAE and Transformers
- Learning Interactive Real-World Simulators
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