The prediction is realized in two stages; the system first computes a number of features from the object and then generates the complete motion trajectory of the center of mass of the object using Long Short Term Memory (LSTM) models.
BRDPN is introduced, a general-purpose learnable physics engine, which enables a robot to predict the effects of its actions in scenes containing groups of ...
Recent success of Deep Learning models have provided big advancements in many fields including robotic manipulation and control.
Missing: effect | Show results with:effect
In this study, we propose a deep model that enables a robot to learn to predict the consequences of its manipulation actions from its own interaction experience ...
In this study, we propose a deep model that enables a robot to learn to predict the consequences of its manipulation actions from its own interaction experience ...
This repository contains code for work on Deep Effect Trajectory Prediction in Robot Manipulation manuscript.
This work is based on Guided Policy Search algorithm that provides an end-to-end robotic learning framework, mapping vision and state configuration directly ...
Feb 4, 2024 · Abstract:We address the problem of (a) predicting the trajectory of an arm reaching motion, based on a few seconds of the motion's onset, ...
Missing: Deep effect
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The primitive dynamics model takes an initial state and the learned sequence of primitives to predict future states (robot and object poses) for the task.
Deep Effect Trajectory Prediction in Robot Manipulation M.Y. Seker, A.E. Tekden, E. Ugur Robotics and Autonomous Systems, RAS 2019. Paper Link · Github Link. An ...