Multimodal Diffusion Forcing trains a diffusion model on partially masked multimodal robot trajectories to learn temporal and cross-modal dependencies for forceful manipulation.
Improved denoising diffusion prob- abilistic models
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MIMIC-D enables multi-modal multi-agent coordination via joint training of decentralized diffusion policies using only local information.
TAX-DPD combines a feed-forward dense GMM for global placement priors with disentangled point cloud diffusion for local geometry and pose to achieve precise robotic object placement.
citing papers explorer
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Multimodal Diffusion Forcing for Forceful Manipulation
Multimodal Diffusion Forcing trains a diffusion model on partially masked multimodal robot trajectories to learn temporal and cross-modal dependencies for forceful manipulation.
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MIMIC-D: Multi-modal Imitation for MultI-agent Coordination with Decentralized Diffusion Policies
MIMIC-D enables multi-modal multi-agent coordination via joint training of decentralized diffusion policies using only local information.
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Disentangled Point Diffusion for Precise Object Placement
TAX-DPD combines a feed-forward dense GMM for global placement priors with disentangled point cloud diffusion for local geometry and pose to achieve precise robotic object placement.