Recent advances of deep robotic affordance learning: a reinforcement learning perspective
As a popular concept proposed in the field of psychology, affordance has been regarded as
one of the important abilities that enable humans to understand and interact with the …
one of the important abilities that enable humans to understand and interact with the …
You only demonstrate once: Category-level manipulation from single visual demonstration
Promising results have been achieved recently in category-level manipulation that
generalizes across object instances. Nevertheless, it often requires expensive real-world …
generalizes across object instances. Nevertheless, it often requires expensive real-world …
Catgrasp: Learning category-level task-relevant grasping in clutter from simulation
Task-relevant grasping is critical for industrial assembly, where downstream manipulation
tasks constrain the set of valid grasps. Learning how to perform this task, however, is …
tasks constrain the set of valid grasps. Learning how to perform this task, however, is …
Heuristics integrated deep reinforcement learning for online 3d bin packing
S Yang, S Song, S Chu, R Song… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Online 3D Bin Packing Problem (3D-BPP) has a wide range of industrial applications and
there is an emerging research interest in learning optimal bin packing policy and deploying …
there is an emerging research interest in learning optimal bin packing policy and deploying …
Packerbot: Variable-sized product packing with heuristic deep reinforcement learning
Z Yang, S Yang, S Song, W Zhang… - 2021 IEEE/RSJ …, 2021 - ieeexplore.ieee.org
Product packing is a typical application in ware-house automation that aims to pick objects
from unstructured piles and place them into bins with optimized placing policy. However, it …
from unstructured piles and place them into bins with optimized placing policy. However, it …
Learning category-level manipulation tasks from point clouds with dynamic graph CNNs
J Liang, A Boularias - 2023 IEEE International Conference on …, 2023 - ieeexplore.ieee.org
This paper presents a new technique for learning category-level manipulation from raw RGB-
D videos of task demonstrations, with no manual labels or annotations. Category-level …
D videos of task demonstrations, with no manual labels or annotations. Category-level …
A hierarchical framework for quadruped locomotion based on reinforcement learning
W Tan, X Fang, W Zhang, R Song… - 2021 IEEE/RSJ …, 2021 - ieeexplore.ieee.org
Quadruped locomotion is a challenging task for learning-based algorithms. It requires
tedious manual tuning and is difficult to deploy in reality due to the reality gap. In this paper …
tedious manual tuning and is difficult to deploy in reality due to the reality gap. In this paper …
A Hierarchical Framework for Quadruped Omnidirectional Locomotion Based on Reinforcement Learning
W Tan, X Fang, W Zhang, R Song… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Quadruped locomotion is challenging for many learning-based algorithms. This is because it
requires tedious manual tuning to cope with different types of terrains and is difficult to …
requires tedious manual tuning to cope with different types of terrains and is difficult to …
Functional grasp transfer across a category of objects from only one labeled instance
To assist or replace human beings in completing various tasks, research on the functional
grasp synthesis of dexterous hands with high degree-of-freedom (DoF) is necessary and …
grasp synthesis of dexterous hands with high degree-of-freedom (DoF) is necessary and …
Learning dense visual descriptors using image augmentations for robot manipulation tasks
We propose a self-supervised training approach for learning view-invariant dense visual
descriptors using image augmentations. Unlike existing works, which often require complex …
descriptors using image augmentations. Unlike existing works, which often require complex …