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Neural joint space implicit signed distance functions for reactive robot manipulator control
In this letter, we present an approach for learning a neural implicit signed distance function
expressed in joint space coordinates, that efficiently computes distance-to-collisions for …
expressed in joint space coordinates, that efficiently computes distance-to-collisions for …
A vision-based human digital twin modeling approach for adaptive human–robot collaboration
Human–robot collaboration (HRC) has been identified as a highly promising paradigm for
human-centric smart manufacturing in the context of Industry 5.0. In order to enhance both …
human-centric smart manufacturing in the context of Industry 5.0. In order to enhance both …
Reach For the Spheres: Tangency-aware surface reconstruction of SDFs
Signed distance fields (SDFs) are a widely used implicit surface representation, with broad
applications in computer graphics, computer vision, and applied mathematics. To …
applications in computer graphics, computer vision, and applied mathematics. To …
Ntfields: Neural time fields for physics-informed robot motion planning
Neural Motion Planners (NMPs) have emerged as a promising tool for solving robot
navigation tasks in complex environments. However, these methods often require expert …
navigation tasks in complex environments. However, these methods often require expert …
Collision-free motion generation based on stochastic optimization and composite signed distance field networks of articulated robot
Safe robot motion generation is critical for practical applications from manufacturing to
homes. In this work, we proposed a stochastic optimization-based motion generation …
homes. In this work, we proposed a stochastic optimization-based motion generation …
Safe reinforcement learning of dynamic high-dimensional robotic tasks: navigation, manipulation, interaction
Safety is a fundamental property for the real-world deployment of robotic platforms. Any
control policy should avoid dangerous actions that could harm the environment, humans, or …
control policy should avoid dangerous actions that could harm the environment, humans, or …
Self-supervised learning of implicit shape representation with dense correspondence for deformable objects
Learning 3D shape representation with dense correspondence for deformable objects is a
fundamental problem in computer vision. Existing approaches often need additional …
fundamental problem in computer vision. Existing approaches often need additional …
Sorotoki: a Matlab toolkit for design, modeling, and control of soft robots
In this paper, we present Sorotoki, an open-source toolkit in MATLAB that offers a
comprehensive suite of tools for the design, modeling, and control of soft robots. The …
comprehensive suite of tools for the design, modeling, and control of soft robots. The …
Safe reinforcement learning on the constraint manifold: Theory and applications
Integrating learning-based techniques, especially reinforcement learning, into robotics is
promising for solving complex problems in unstructured environments. However, most …
promising for solving complex problems in unstructured environments. However, most …
Representing robot geometry as distance fields: Applications to whole-body manipulation
In this work, we propose a novel approach to represent robot geometry as distance fields
(RDF) that extends the principle of signed distance fields (SDFs) to articulated kinematic …
(RDF) that extends the principle of signed distance fields (SDFs) to articulated kinematic …