Online Estimation of Articulated Objects with Factor Graphs using Vision and Proprioceptive Sensing

Understand

From dishwashers to cabinets, humans interact with articulated objects every day, and for a robot to assist in common manipulation tasks, it must learn a representation of articulation.

• Recent deep learning learning methods can provide powerful vision-based priors on the affordance of articulated objects from previous, possibly simulated, experiences.
• In contrast, many works estimate articulation by observing the object in motion, requiring the robot to already be interacting with the object.
• In this work, we propose to use the best of both worlds by introducing an online estimation method that merges vision-based affordance predictions from a neural network with interactive kinematic sensing in an analytical model.