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Found 6 papers, 1 papers with code
Learning to Represent Haptic Feedback for Partially-Observable Tasks
The sense of touch, being the earliest sensory system to develop in a human body [1], plays a critical part of our daily interaction with the environment.
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.
Robobarista: Object Part based Transfer of Manipulation Trajectories from Crowd-sourcing in 3D Pointclouds
We formulate the manipulation planning as a structured prediction problem and design a deep learning model that can handle large noise in the manipulation demonstrations and learns features from three different modalities: point-clouds, language and trajectory.
Synthesizing Manipulation Sequences for Under-Specified Tasks using Unrolled Markov Random Fields
Many tasks in human environments require performing a sequence of navigation and manipulation steps involving objects.
Unstructured Human Activity Detection from RGBD Images
Being able to detect and recognize human activities is essential for several applications, including personal assistive robotics.
