DreamPolicy integrates an autoregressive diffusion world model with policy learning to produce a single scalable policy that generalizes to unseen composite terrains for humanoid locomotion.
Robotkeyframing: Learning locomotion with high-level objectives via mixture of dense and sparse rewards
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.RO 2years
2025 2verdicts
UNVERDICTED 2representative citing papers
A single goal-conditioned RL policy trained on contact plans performs multiple gaits and bimanual manipulation tasks on quadruped and humanoid robots.
citing papers explorer
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DreamPolicy: A Unified World-model Policy for Scalable Humanoid Locomotion
DreamPolicy integrates an autoregressive diffusion world model with policy learning to produce a single scalable policy that generalizes to unseen composite terrains for humanoid locomotion.
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Learning to Act Through Contact: A Unified View of Multi-Task Robot Learning
A single goal-conditioned RL policy trained on contact plans performs multiple gaits and bimanual manipulation tasks on quadruped and humanoid robots.