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Found 6 papers, 1 papers with code
DiffusionNOCS: Managing Symmetry and Uncertainty in Sim2Real Multi-Modal Category-level Pose Estimation
This paper addresses the challenging problem of category-level pose estimation.
GS-Pose: Category-Level Object Pose Estimation via Geometric and Semantic Correspondence
This requires significantly less data to train than prior methods since the semantic features are robust to object texture and appearance.
Learning Fabric Manipulation in the Real World with Human Videos
A promising alternative is to learn fabric manipulation directly from watching humans perform the task.
Evaluating task-agnostic exploration for fixed-batch learning of arbitrary future tasks
Deep reinforcement learning has been shown to solve challenging tasks where large amounts of training experience is available, usually obtained online while learning the task.
Mirroring to Build Trust in Digital Assistants
We describe experiments towards building a conversational digital assistant that considers the preferred conversational style of the user.
Sim-to-Real Transfer of Robot Learning with Variable Length Inputs
Current end-to-end deep Reinforcement Learning (RL) approaches require jointly learning perception, decision-making and low-level control from very sparse reward signals and high-dimensional inputs, with little capability of incorporating prior knowledge.
