Review of reinforcement learning for robotic grasping: Analysis and recommendations
This review paper provides a comprehensive analysis of over 100 research papers focused
on the challenges of robotic grasping and the effectiveness of various machine learning …
on the challenges of robotic grasping and the effectiveness of various machine learning …
[HTML][HTML] Opportunities and challenges in applying reinforcement learning to robotic manipulation: An industrial case study
As moves towards a more agile paradigm, industrial robots are expected to perform more
complex tasks in less structured environments, complicating the use of traditional …
complex tasks in less structured environments, complicating the use of traditional …
HACMan: Learning hybrid actor-critic maps for 6D non-prehensile manipulation
Manipulating objects without grasping them is an essential component of human dexterity,
referred to as non-prehensile manipulation. Non-prehensile manipulation may enable more …
referred to as non-prehensile manipulation. Non-prehensile manipulation may enable more …
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, ie, objects …
the exploratory grasp learning during online adaptation to new picking scenarios, ie, objects …
HACMan++: Spatially-Grounded Motion Primitives for Manipulation
Although end-to-end robot learning has shown some success for robot manipulation, the
learned policies are often not sufficiently robust to variations in object pose or geometry. To …
learned policies are often not sufficiently robust to variations in object pose or geometry. To …
Hierarchical policy learning for mechanical search
Retrieving objects from clutters is a complex task, which requires multiple interactions with
the environment until the target object can be extracted. These interactions involve …
the environment until the target object can be extracted. These interactions involve …
An Improved SAC-Based Deep Reinforcement Learning Framework for Collaborative Pushing and Grasping in Underwater Environments
J Gao, Y Li, Y Chen, Y He, J Guo - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Autonomous grasping is a fundamental task for underwater robots, but direct grasping for
tightly stacked objects will lead to collisions and grasp failures, which requires pushing …
tightly stacked objects will lead to collisions and grasp failures, which requires pushing …
PRF: A Program Reuse Framework for Automated Programming by Learning from Existing Robot Programs
This paper explores the problem of automated robot program generation from limited
historical data when neither accurate geometric environmental models nor online vision …
historical data when neither accurate geometric environmental models nor online vision …
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 …
the dynamic grasp learning that occurs during real-time adaptation to novel picking …
DMFC-GraspNet: Differentiable Multi-Fingered Robotic Grasp Generation in Cluttered Scenes
P Blättner, J Brand, G Neumann, NA Vien - arXiv preprint arXiv …, 2023 - arxiv.org
Robotic grasping is a fundamental skill required for object manipulation in robotics. Multi-
fingered robotic hands, which mimic the structure of the human hand, can potentially perform …
fingered robotic hands, which mimic the structure of the human hand, can potentially perform …