Deep learning approaches to grasp synthesis: A review
Gras** is the process of picking up an object by applying forces and torques at a set of
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …
A survey on learning-based robotic gras**
Abstract Purpose of Review This review provides a comprehensive overview of machine
learning approaches for vision-based robotic gras** and manipulation. Current trends and …
learning approaches for vision-based robotic gras** and manipulation. Current trends and …
Autonomous navigation of stratospheric balloons using reinforcement learning
Efficiently navigating a superpressure balloon in the stratosphere requires the integration of
a multitude of cues, such as wind speed and solar elevation, and the process is complicated …
a multitude of cues, such as wind speed and solar elevation, and the process is complicated …
Solving rubik's cube with a robot hand
I Akkaya, M Andrychowicz, M Chociej, M Litwin… - ar** using generative residual convolutional neural network
In this paper, we present a modular robotic system to tackle the problem of generating and
performing antipodal robotic grasps for unknown objects from the n-channel image of the …
performing antipodal robotic grasps for unknown objects from the n-channel image of the …
Acronym: A large-scale grasp dataset based on simulation
We introduce ACRONYM, a dataset for robot grasp planning based on physics simulation.
The dataset contains 17.7 M parallel-jaw grasps, spanning 8872 objects from 262 different …
The dataset contains 17.7 M parallel-jaw grasps, spanning 8872 objects from 262 different …
Structured world models from human videos
We tackle the problem of learning complex, general behaviors directly in the real world. We
propose an approach for robots to efficiently learn manipulation skills using only a handful of …
propose an approach for robots to efficiently learn manipulation skills using only a handful of …