RLA-WM predicts residual latent actions via flow matching to create visual feature world models that outperform prior feature-based and diffusion approaches while enabling offline video-based robot RL.
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2026 3verdicts
UNVERDICTED 3representative citing papers
Dream-MPC boosts underlying policies on 24 continuous control tasks by optimizing policy-generated trajectories with gradient ascent, uncertainty regularization, and temporal amortization inside a latent world model.
Facial emotion embeddings improve short-term pose forecasting accuracy for emotion-driven motions when fused via normalized gating in a lightweight LSTM world model, but not with simple multimodal fusion.
citing papers explorer
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Learning Visual Feature-Based World Models via Residual Latent Action
RLA-WM predicts residual latent actions via flow matching to create visual feature world models that outperform prior feature-based and diffusion approaches while enabling offline video-based robot RL.
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Dream-MPC: Gradient-Based Model Predictive Control with Latent Imagination
Dream-MPC boosts underlying policies on 24 continuous control tasks by optimizing policy-generated trajectories with gradient ascent, uncertainty regularization, and temporal amortization inside a latent world model.
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Emotion-Conditioned Short-Horizon Human Pose Forecasting with a Lightweight Predictive World Model
Facial emotion embeddings improve short-term pose forecasting accuracy for emotion-driven motions when fused via normalized gating in a lightweight LSTM world model, but not with simple multimodal fusion.