GRAFT transfers manipulation contact points to unseen objects via part-graph retrieval and correspondence from a single demonstration.
Ram: Retrieval-based affordance transfer for generalizable zero-shot robotic manipulation
7 Pith papers cite this work. Polarity classification is still indexing.
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AffordanceVLA proposes a VLA model with affordance-aware modules (Which2Act, Where2Act, How2Act) in a Mixture-of-Transformer trained in three stages to improve robotic manipulation.
A task-conditioned two-stage system decouples grasp localization from interaction trajectory planning using specialized foundation models to improve generalization across heterogeneous object types.
AffordGen synthesizes large-scale affordance-aware manipulation trajectories via keypoint correspondence on 3D meshes, enabling zero-shot visuomotor policies for unseen objects from few source demos.
PALM improves long-horizon robotic manipulation success by distilling affordance representations for object interaction and predicting within-subtask progress in a VLA model.
ViTacFormer learns a cross-modal visuo-tactile latent space with autoregressive tactile prediction and an easy-to-hard curriculum, then uses the representation for imitation learning that yields ~50% higher success and the first reported 11-stage, 2.5-minute autonomous dexterous tasks.
ReGIL retrieves segments from a single demonstration to compute local temporal-alignment rewards and guide policy training, achieving >75% success on three real-robot tasks with <1 hour of online data.
citing papers explorer
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GRAFT: Graph-Based Affordance Transfer via Part Correspondence
GRAFT transfers manipulation contact points to unseen objects via part-graph retrieval and correspondence from a single demonstration.
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AffordanceVLA: A Vision-Language-Action Model Empowering Action Generation through Affordance-Aware Understanding
AffordanceVLA proposes a VLA model with affordance-aware modules (Which2Act, Where2Act, How2Act) in a Mixture-of-Transformer trained in three stages to improve robotic manipulation.
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HeteroGenManip: Generalizable Manipulation For Heterogeneous Object Interactions
A task-conditioned two-stage system decouples grasp localization from interaction trajectory planning using specialized foundation models to improve generalization across heterogeneous object types.
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AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence
AffordGen synthesizes large-scale affordance-aware manipulation trajectories via keypoint correspondence on 3D meshes, enabling zero-shot visuomotor policies for unseen objects from few source demos.
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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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ViTacFormer: Learning Cross-Modal Representation for Visuo-Tactile Dexterous Manipulation
ViTacFormer learns a cross-modal visuo-tactile latent space with autoregressive tactile prediction and an easy-to-hard curriculum, then uses the representation for imitation learning that yields ~50% higher success and the first reported 11-stage, 2.5-minute autonomous dexterous tasks.
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ReGIL: Retrieval-Guided Imitation Learning from a Single Demonstration
ReGIL retrieves segments from a single demonstration to compute local temporal-alignment rewards and guide policy training, achieving >75% success on three real-robot tasks with <1 hour of online data.