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Found 10 papers, 1 papers with code
Leverage Task Context for Object Affordance Ranking
To enable agents to develop a deeper understanding of the objects required to perform tasks, we propose to leverage task context for object affordance ranking, i. e., given image of a complex scene and the textual description of the affordance and task context, revealing task-object relationships and clarifying the priority rank of detected objects.
MATCH POLICY: A Simple Pipeline from Point Cloud Registration to Manipulation Policies
MATCH POLICY is designed to solve high-precision tasks with a key-frame setting.
Equivariant Diffusion Policy
Recent work has shown diffusion models are an effective approach to learning the multimodal distributions arising from demonstration data in behavior cloning.
Imagination Policy: Using Generative Point Cloud Models for Learning Manipulation Policies
Humans can imagine goal states during planning and perform actions to match those goals.
Fourier Transporter: Bi-Equivariant Robotic Manipulation in 3D
Many complex robotic manipulation tasks can be decomposed as a sequence of pick and place actions.
Leveraging Symmetries in Pick and Place
This paper analytically studies the symmetries present in planar robotic pick and place and proposes a method of incorporating equivariant neural models into Transporter Net in a way that captures all symmetries.
Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detection
Given point cloud input, the problem of 6-DoF grasp pose detection is to identify a set of hand poses in SE(3) from which an object can be successfully grasped.
Contrastive learning-based computational histopathology predict differential expression of cancer driver genes
We leveraged contrastive learning on large-scale unannotated WSIs to derive slide-level histopathological feature in latent space, and then transfer it to tumor diagnosis and prediction of differentially expressed cancer driver genes.
GASCN: Graph Attention Shape Completion Network
Shape completion, the problem of inferring the complete geometry of an object given a partial point cloud, is an important problem in robotics and computer vision.
Representation Evaluation Block-based Teacher-Student Network for the Industrial Quality-relevant Performance Modeling and Monitoring
Quality-relevant fault detection plays an important role in industrial processes, while the current quality-related fault detection methods based on neural networks main concentrate on process-relevant variables and ignore quality-relevant variables, which restrict the application of process monitoring.
