9 citations · 9 across the 4 of their papers we have counts for
4 papers · 1 filter
Distributional Depth-Based Estimation of Object Articulation Models
Ajinkya Jain, Stephen Giguere, Rudolf Lioutikov +1
We propose a method that efficiently learns distributions over articulation model parameters directly from depth images without the need to know articulation model categories a pri…
ScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theory
Ajinkya Jain, Rudolf Lioutikov, Caleb Chuck +1
Robots in human environments will need to interact with a wide variety of articulated objects such as cabinets, drawers, and dishwashers while assisting humans in performing day-to…
Learning Hybrid Object Kinematics for Efficient Hierarchical Planning Under Uncertainty
Ajinkya Jain, Scott Niekum
Sudden changes in the dynamics of robotic tasks, such as contact with an object or the latching of a door, are often viewed as inconvenient discontinuities that make manipulation d…
Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics
Ajinkya Jain, Scott Niekum
Noisy observations coupled with nonlinear dynamics pose one of the biggest challenges in robot motion planning. By decomposing nonlinear dynamics into a discrete set of local dynam…