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Robobarista: Learning to Manipulate Novel Objects via Deep Multimodal Embedding
There is a large variety of objects and appliances in human environments, such as stoves, coffee dispensers, juice extractors, and so on.
Deep Multimodal Embedding: Manipulating Novel Objects with Point-clouds, Language and Trajectories
A robot operating in a real-world environment needs to perform reasoning over a variety of sensor modalities such as vision, language and motion trajectories.
Deep Learning for Detecting Robotic Grasps
We consider the problem of detecting robotic grasps in an RGB-D view of a scene containing objects.
Ranked #7 on
Robotic Grasping
on Cornell Grasp Dataset
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