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Found 5 papers, 0 papers with code
AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer
Addressing this issue, we propose AdaptSim, a new task-driven adaptation framework for sim-to-real transfer that aims to optimize task performance in target (real) environments -- instead of matching dynamics between simulation and reality.
Grounding Language Attributes to Objects using Bayesian Eigenobjects
Our system is able to disambiguate between novel objects, observed via depth images, based on natural language descriptions.
Probabilistic Category-Level Pose Estimation via Segmentation and Predicted-Shape Priors
We introduce a new method for category-level pose estimation which produces a distribution over predicted poses by integrating 3D shape estimates from a generative object model with segmentation information.
Hybrid Bayesian Eigenobjects: Combining Linear Subspace and Deep Network Methods for 3D Robot Vision
We introduce Hybrid Bayesian Eigenobjects (HBEOs), a novel representation for 3D objects designed to allow a robot to jointly estimate the pose, class, and full 3D geometry of a novel object observed from a single viewpoint in a single practical framework.
