Abstract: The inverse dynamics of the six degree-of-freedom (6-DOF) parallel robot (PR) presents an inherent complexity due to the closed-loop kinematic chains. To derive computational efficient ...

The LIM assumes the relevant dynamics can be represented as a linear system forced by stochastic noise (Hasselmann, 1988; Penland & Sardeshmukh, 1995), and written in the form of a linear stochastic ...

Imitation learning teaches AI agents by example: show the agent recordings of how people perform a task and let it infer what to do. The most common approach, Behavior Cloning (BC), frames this as a ...

NaVIDA is a lightweight Vision-Language Navigation (VLN) framework that incorporates inverse dynamics supervision as an explicit objective to embed action-grounded visual dynamics into policy learning ...

Differential equations are fundamental tools in physics: they are used to describe phenomena ranging from fluid dynamics to general relativity. But when these equations become stiff (i.e. they involve ...

Department of Chemical and Biomolecular Engineering, University of Delaware, Colburn Lab, 150 Academy Street, Newark, Delaware 19716, United States Department of Materials Science and Engineering, ...

Purpose: Inverse-dynamics (ID) analysis is an approach widely used for studying spine biomechanics and the estimation of muscle forces. Despite the increasing structural complexity of spine models, ID ...

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One key ingredient in deep learning is the stochastic gradient descent (SGD) algorithm, which allows neural nets to find generalizable solutions at flat minima of the high-dimensional loss function.

Abstract: Inverse dynamics models have been used in robot control algorithms to realize a desired motion or to enhance a robot's performance. As robot dynamics and their operating environments become ...