DreamGen trains robot policies on synthetic trajectories from adapted video world models, enabling a humanoid robot to perform 22 new behaviors in seen and unseen environments from a single pick-and-place teleoperation dataset.
Learning manipulation by predicting interaction
6 Pith papers cite this work. Polarity classification is still indexing.
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HAT-4D presents an agentic VLM-plus-human-in-the-loop pipeline for monocular 4D multi-object interaction reconstruction and releases the MVOIK-4D benchmark.
PALM improves long-horizon robotic manipulation success by distilling affordance representations for object interaction and predicting within-subtask progress in a VLA model.
EgoVLA pretrains VLA models on egocentric human videos, retargets predicted actions to robots via IK, and fine-tunes on few robot demos to improve bimanual manipulation performance on a new simulation benchmark.
FLARE integrates predictive latent world modeling into diffusion transformer policies for robots, delivering up to 26% gains on multitask manipulation benchmarks and enabling co-training with action-free human videos.
citing papers explorer
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DreamGen: Unlocking Generalization in Robot Learning through Video World Models
DreamGen trains robot policies on synthetic trajectories from adapted video world models, enabling a humanoid robot to perform 22 new behaviors in seen and unseen environments from a single pick-and-place teleoperation dataset.
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HAT-4D: Lifting Monocular Video for 4D Multi-Object Interactions via Human-Agent Collaboration
HAT-4D presents an agentic VLM-plus-human-in-the-loop pipeline for monocular 4D multi-object interaction reconstruction and releases the MVOIK-4D benchmark.
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PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation
PALM improves long-horizon robotic manipulation success by distilling affordance representations for object interaction and predicting within-subtask progress in a VLA model.
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EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos
EgoVLA pretrains VLA models on egocentric human videos, retargets predicted actions to robots via IK, and fine-tunes on few robot demos to improve bimanual manipulation performance on a new simulation benchmark.
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FLARE: Robot Learning with Implicit World Modeling
FLARE integrates predictive latent world modeling into diffusion transformer policies for robots, delivering up to 26% gains on multitask manipulation benchmarks and enabling co-training with action-free human videos.
- Action-Effect Memory Pretraining for Robot Manipulation