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Found 19 papers, 6 papers with code
Pseudo-Labeling and Contextual Curriculum Learning for Online Grasp Learning in Robotic Bin Picking
The prevailing grasp prediction methods predominantly rely on offline learning, overlooking the dynamic grasp learning that occurs during real-time adaptation to novel picking scenarios.
Uncertainty-driven Exploration Strategies for Online Grasp Learning
Existing grasp prediction approaches are mostly based on offline learning, while, ignoring the exploratory grasp learning during online adaptation to new picking scenarios, i. e., objects that are unseen or out-of-domain (OOD), camera and bin settings, etc.
SA6D: Self-Adaptive Few-Shot 6D Pose Estimator for Novel and Occluded Objects
To enable meaningful robotic manipulation of objects in the real-world, 6D pose estimation is one of the critical aspects.
DMFC-GraspNet: Differentiable Multi-Fingered Robotic Grasp Generation in Cluttered Scenes
The results demonstrate the effectiveness of the proposed approach in predicting versatile and dense grasps, and in advancing the field of multi-fingered robotic grasping.
Model-free Grasping with Multi-Suction Cup Grippers for Robotic Bin Picking
This paper presents a novel method for model-free prediction of grasp poses for suction grippers with multiple suction cups.
SyMFM6D: Symmetry-aware Multi-directional Fusion for Multi-View 6D Object Pose Estimation
Detecting objects and estimating their 6D poses is essential for automated systems to interact safely with the environment.
Deep Black-Box Reinforcement Learning with Movement Primitives
In this paper, we present a new algorithm for deep ERL.
FusionVAE: A Deep Hierarchical Variational Autoencoder for RGB Image Fusion
Sensor fusion can significantly improve the performance of many computer vision tasks.
Meta-Learning Regrasping Strategies for Physical-Agnostic Objects
Grasping inhomogeneous objects in real-world applications remains a challenging task due to the unknown physical properties such as mass distribution and coefficient of friction.
What Matters For Meta-Learning Vision Regression Tasks?
To this end, we (i) exhaustively evaluate common meta-learning techniques on these tasks, and (ii) quantitatively analyze the effect of various deep learning techniques commonly used in recent meta-learning algorithms in order to strengthen the generalization capability: data augmentation, domain randomization, task augmentation and meta-regularization.
A Hybrid Approach for Learning to Shift and Grasp with Elaborate Motion Primitives
Many possible fields of application of robots in real world settings hinge on the ability of robots to grasp objects.
Non-local Graph Convolutional Network for joint Activity Recognition and Motion Prediction
3D skeleton-based motion prediction and activity recognition are two interwoven tasks in human behaviour analysis.
Differentiable Robust LQR Layers
This paper proposes a differentiable robust LQR layer for reinforcement learning and imitation learning under model uncertainty and stochastic dynamics.
Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertainty
We propose a new formulation that addresses these limitations by also modifying the feedback signals to the controller with an RL policy and show superior performance of our approach on a contact-rich peg-insertion task under position and orientation uncertainty.
Differentiable Trust Region Layers for Deep Reinforcement Learning
However, enforcing such trust regions in deep reinforcement learning is difficult.
Bayes-Adaptive Deep Model-Based Policy Optimisation
We introduce a Bayesian (deep) model-based reinforcement learning method (RoMBRL) that can capture model uncertainty to achieve sample-efficient policy optimisation.
Deep Energy Autoencoder for Noncoherent Multicarrier MU-SIMO Systems
We propose a novel deep energy autoencoder (EA) for noncoherent multicarrier multiuser single-input multipleoutput (MU-SIMO) systems under fading channels.
Information Theory Signal Processing Information Theory
Deep Hierarchical Reinforcement Learning Algorithm in Partially Observable Markov Decision Processes
Hierarchical reinforcement learning is a principled approach that is able to tackle these challenging tasks.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features
To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection.
