Hybrid diffusion jointly generates symbolic plans and continuous trajectories for robotic planning, outperforming standard diffusion models on long-horizon tasks.
The ingredients for robotic diffusion transformers
6 Pith papers cite this work. Polarity classification is still indexing.
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DSRL steers pretrained diffusion policies for robotics by applying RL to their latent noise inputs, achieving sample-efficient real-world adaptation with only black-box access.
A1 is a transparent VLA framework achieving state-of-the-art robot manipulation success with up to 72% lower latency via adaptive layer truncation and inter-layer flow matching.
Unified World Models couple video and action diffusion inside one transformer with independent timesteps, enabling pretraining on heterogeneous robot datasets that include action-free video and producing more generalizable policies than imitation learning alone.
DexVLA combines a scaled diffusion action expert with embodiment curriculum learning to achieve better generalization and performance than prior VLA models on diverse robot hardware and long-horizon tasks.
Key-Gram uses a memory module with key-grams and hashed lookup to inject static linguistic priors into vision-language-action backbones, yielding reported gains on manipulation benchmarks.
citing papers explorer
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Hybrid Diffusion for Simultaneous Symbolic and Continuous Planning
Hybrid diffusion jointly generates symbolic plans and continuous trajectories for robotic planning, outperforming standard diffusion models on long-horizon tasks.
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Steering Your Diffusion Policy with Latent Space Reinforcement Learning
DSRL steers pretrained diffusion policies for robotics by applying RL to their latent noise inputs, achieving sample-efficient real-world adaptation with only black-box access.
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A1: A Fully Transparent Open-Source, Adaptive and Efficient Truncated Vision-Language-Action Model
A1 is a transparent VLA framework achieving state-of-the-art robot manipulation success with up to 72% lower latency via adaptive layer truncation and inter-layer flow matching.
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Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets
Unified World Models couple video and action diffusion inside one transformer with independent timesteps, enabling pretraining on heterogeneous robot datasets that include action-free video and producing more generalizable policies than imitation learning alone.
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DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control
DexVLA combines a scaled diffusion action expert with embodiment curriculum learning to achieve better generalization and performance than prior VLA models on diverse robot hardware and long-horizon tasks.
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Key-Gram: Extensible World Knowledge for Embodied Manipulation
Key-Gram uses a memory module with key-grams and hashed lookup to inject static linguistic priors into vision-language-action backbones, yielding reported gains on manipulation benchmarks.