Accurate representation of articulated motion is a challenging problem for machine perception. Several successful tracking algorithms have been developed that model human body as an articulated tree. We propose a learning-based method for creating such articulated models from observations of multiple rigid motions. This paper is concerned with recovering topology of the articulated model, when ...

This paper addresses the subspace properties and the recovery of articulated motion. We point out that the nature of the motion subspace of an articulated object is a combination of a number of intersecting rigid motion subspaces. The rank of that motion subspace is less than that of each articulated part combined, depending on the connection between every two linked parts, either a rotation ax...

We introduce the Universal Manipulation Policy Network (UMPNet) – a single image-based policy network that infers closed-loop action sequences for manipulating articulated objects. To infer wide range of trajectories, supports 6DoF representation and varying trajectory length. handle diverse set objects, learns from objects with different articulation structures generalizes to unseen or categor...

Articulated robot arms offer maximum positioning flexibility but suffer from complex kinematics. In most applications, linear motion is desirable. Calculating the kinematic equations which govern an articulated arm is straight forward; however, it is generally difJicult to calculate the inverse kinematic equations that are needed to position the arm in closed form. Using a fuzzy reasoning syste...