ECD reformulates compositional diffusion planning as energy minimization over local bridge potentials, adding a boundary reaction term and a Markov score approximation that runs in linear time.
Simple hierarchical planning with diffusion
8 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 8representative citing papers
CGPO integrates training-free critic guidance into diffusion denoising to produce high-Q actions as regression targets, yielding SOTA results on MuJoCo locomotion and successful Franka arm grasping.
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.
Conditional Graph Diffusion generates continuous negotiation outcomes with high individual rationality using GATv2 encoders, cross-attention fusion, and inference-time normative guidance gradients.
AR Forcing trains diffusion world models by integrating standard noise prediction loss into an autoregressive loop that uses self-generated predictions as context, reducing train-inference mismatch for improved long-horizon image consistency and trajectory accuracy on navigation datasets.
RCD steers compositional diffusion sampling toward high-density coherent plans by combining reconstruction-error guidance with overlap consistency, outperforming prior methods on locomotion, manipulation, and pixel-based long-horizon tasks.
WorldDP combines a high-level object-centric world model for subgoal planning with a low-level diffusion policy for execution, claiming better performance than baselines on multi-stage robotic manipulation benchmarks.
Co-policy framework uses a fine-tuned Qwen-vl planner for semantic plans and a Gaussian-mixture visuomotor policy for low-latency robot actions to enable constrained human-robot musical co-creation.
citing papers explorer
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Energy-based Compositional Diffusion Planning
ECD reformulates compositional diffusion planning as energy minimization over local bridge potentials, adding a boundary reaction term and a Markov score approximation that runs in linear time.
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Sample-Efficient Diffusion-based Reinforcement Learning with Critic Guidance
CGPO integrates training-free critic guidance into diffusion denoising to produce high-Q actions as regression targets, yielding SOTA results on MuJoCo locomotion and successful Franka arm grasping.
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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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Conditional Graph Diffusion for Negotiation Support: Overcoming Discrete Infeasibility and Preference Elicitation Gaps
Conditional Graph Diffusion generates continuous negotiation outcomes with high individual rationality using GATv2 encoders, cross-attention fusion, and inference-time normative guidance gradients.
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AR Forcing: Towards Long-Horizon Robot Navigation World Model
AR Forcing trains diffusion world models by integrating standard noise prediction loss into an autoregressive loop that uses self-generated predictions as context, reducing train-inference mismatch for improved long-horizon image consistency and trajectory accuracy on navigation datasets.
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Refining Compositional Diffusion for Reliable Long-Horizon Planning
RCD steers compositional diffusion sampling toward high-density coherent plans by combining reconstruction-error guidance with overlap consistency, outperforming prior methods on locomotion, manipulation, and pixel-based long-horizon tasks.
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Unifying Object-Centric World Models and Diffusion Policy: A Hierarchical Framework for Multi-Stage Robotic Tasks
WorldDP combines a high-level object-centric world model for subgoal planning with a low-level diffusion policy for execution, claiming better performance than baselines on multi-stage robotic manipulation benchmarks.
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Co-policy: Responsive Human-Robot Co-Creation for Musical Performances
Co-policy framework uses a fine-tuned Qwen-vl planner for semantic plans and a Gaussian-mixture visuomotor policy for low-latency robot actions to enable constrained human-robot musical co-creation.