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Found 18 papers, 6 papers with code
EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation
We present the Evolved Grasping Analysis Dataset (EGAD), comprising over 2000 generated objects aimed at training and evaluating robotic visual grasp detection algorithms.
Robotic Grasping
Robotics
Evaluating task-agnostic exploration for fixed-batch learning of arbitrary future tasks
Deep reinforcement learning has been shown to solve challenging tasks where large amounts of training experience is available, usually obtained online while learning the task.
A Perceived Environment Design using a Multi-Modal Variational Autoencoder for learning Active-Sensing
This contribution comprises the interplay between a multi-modal variational autoencoder and an environment to a perceived environment, on which an agent can act.
Ctrl-Z: Recovering from Instability in Reinforcement Learning
When learning behavior, training data is often generated by the learner itself; this can result in unstable training dynamics, and this problem has particularly important applications in safety-sensitive real-world control tasks such as robotics.
Deep Generative Models for learning Coherent Latent Representations from Multi-Modal Data
The application of multi-modal generative models by means of a Variational Auto Encoder (VAE) is an upcoming research topic for sensor fusion and bi-directional modality exchange.
Quantifying the Reality Gap in Robotic Manipulation Tasks
We quantify the accuracy of various simulators compared to a real world robotic reaching and interaction task.
Robotics
Multi-View Picking: Next-best-view Reaching for Improved Grasping in Clutter
Camera viewpoint selection is an important aspect of visual grasp detection, especially in clutter where many occlusions are present.
Robotics
Sim-to-Real Transfer of Robot Learning with Variable Length Inputs
Current end-to-end deep Reinforcement Learning (RL) approaches require jointly learning perception, decision-making and low-level control from very sparse reward signals and high-dimensional inputs, with little capability of incorporating prior knowledge.
Coordinated Heterogeneous Distributed Perception based on Latent Space Representation
We investigate a reinforcement approach for distributed sensing based on the latent space derived from multi-modal deep generative models.
The Limits and Potentials of Deep Learning for Robotics
In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep learning.
Robotics
Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach
This paper presents a real-time, object-independent grasp synthesis method which can be used for closed-loop grasping.
Ranked #6 on
Robotic Grasping
on Cornell Grasp Dataset
Robotics
Adversarial Discriminative Sim-to-real Transfer of Visuo-motor Policies
Policies can be transferred to real environments with only 93 labelled and 186 unlabelled real images.
Visual Servoing from Deep Neural Networks
We present a deep neural network-based method to perform high-precision, robust and real-time 6 DOF visual servoing.
Tuning Modular Networks with Weighted Losses for Hand-Eye Coordination
This paper introduces an end-to-end fine-tuning method to improve hand-eye coordination in modular deep visuo-motor policies (modular networks) where each module is trained independently.
Modular Deep Q Networks for Sim-to-real Transfer of Visuo-motor Policies
While deep learning has had significant successes in computer vision thanks to the abundance of visual data, collecting sufficiently large real-world datasets for robot learning can be costly.
The ACRV Picking Benchmark (APB): A Robotic Shelf Picking Benchmark to Foster Reproducible Research
We present a new physical benchmark challenge for robotic picking: the ACRV Picking Benchmark (APB).
Richardson-Lucy Deblurring for Moving Light Field Cameras
The method deals correctly with blur caused by 6-degree-of-freedom camera motion in complex 3-D scenes, without performing depth estimation.
Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control
This paper introduces a machine learning based system for controlling a robotic manipulator with visual perception only.
