52 citations · 80 across the 11 of their papers we have counts for
13 papers
Enhanced Probabilistic Collision Detection for Motion Planning Under Sensing Uncertainty
Xiaoli Wang, Sipu Ruan, Xin Meng +1
Probabilistic collision detection (PCD) is essential in motion planning for robots operating in unstructured environments, where considering sensing uncertainty helps prevent damag…
Prepare the Chair for the Bear! Robot Imagination of Sitting Affordance to Reorient Previously Unseen Chairs
Xin Meng, Hongtao Wu, Sipu Ruan +1
In this letter, a paradigm for the classification and manipulation of previously unseen objects is established and demonstrated through a real example of chairs. We present a novel…
PRIMP: PRobabilistically-Informed Motion Primitives for Efficient Affordance Learning from Demonstration
Sipu Ruan, Weixiao Liu, Xiaoli Wang +2
This paper proposes a learning-from-demonstration method using probability densities on the workspaces of robot manipulators. The method, named "PRobabilistically-Informed Motion P…
Marching-Primitives: Shape Abstraction from Signed Distance Function
Weixiao Liu, Yuwei Wu, Sipu Ruan +1
Representing complex objects with basic geometric primitives has long been a topic in computer vision. Primitive-based representations have the merits of compactness and computatio…
Primitive-based Shape Abstraction via Nonparametric Bayesian Inference
Yuwei Wu, Weixiao Liu, Sipu Ruan +1
3D shape abstraction has drawn great interest over the years. Apart from low-level representations such as meshes and voxels, researchers also seek to semantically abstract complex…
Robust and Accurate Superquadric Recovery: a Probabilistic Approach
Weixiao Liu, Yuwei Wu, Sipu Ruan +1
Interpreting objects with basic geometric primitives has long been studied in computer vision. Among geometric primitives, superquadrics are well known for their ability to represe…
Put the Bear on the Chair! Intelligent Robot Interaction with Previously Unseen Chairs via Robot Imagination
Hongtao Wu, Xin Meng, Sipu Ruan +1
In this paper, we study the problem of autonomously seating a teddy bear on a previously unseen chair. To achieve this goal, we present a novel method for robots to imagine the sit…
Efficient Path Planning in Narrow Passages for Robots with Ellipsoidal Components
Sipu Ruan, Karen L. Poblete, Hongtao Wu +2
Path planning has long been one of the major research areas in robotics, with PRM and RRT being two of the most effective classes of planners. Though generally very efficient, thes…
Closed-Form Minkowski Sums of Convex Bodies with Smooth Positively Curved Boundaries
Sipu Ruan, Gregory S. Chirikjian
This article derives closed-form parametric formulas for the Minkowski sums of convex bodies in d-dimensional Euclidean space with boundaries that are smooth and have all positive…
Quotienting Impertinent Camera Kinematics for 3D Video Stabilization
Thomas W. Mitchel, Christian Wuelker, Jin Seob Kim +2
With the recent advent of methods that allow for real-time computation, dense 3D flows have become a viable basis for fast camera motion estimation. Most importantly, dense flows a…
Quantizing Euclidean motions via double-coset decomposition
Christian Wülker, Gregory S. Chirikjian
Concepts from mathematical crystallography and group theory are used here to quantize the group of rigid-body motions, resulting in a "motion alphabet" with which to express robot…
Signal Alignment for Humanoid Skeletons via the Globally Optimal Reparameterization Algorithm
Thomas W. Mitchel, Sipu Ruan, Gregory S. Chirikjian
The general ability to analyze and classify the 3D kinematics of the human form is an essential step in the development of socially adept humanoid robots. A variety of different ty…
The Globally Optimal Reparameterization Algorithm: an Alternative to Fast Dynamic Time Warping for Action Recognition in Video Sequences
Thomas Mitchel, Sipu Ruan, Yixin Gao +1
Signal alignment has become a popular problem in robotics due in part to its fundamental role in action recognition. Currently, the most successful algorithms for signal alignment…