Recent trends in task and motion planning for robotics: A survey
Autonomous robots are increasingly served in real-world unstructured human environments
with complex long-horizon tasks, such as restaurant serving and office delivery. Task and …
with complex long-horizon tasks, such as restaurant serving and office delivery. Task and …
Socialized learning: A survey of the paradigm shift for edge intelligence in networked systems
Amidst the robust impetus from artificial intelligence (AI) and big data, edge intelligence (EI)
has emerged as a nascent computing paradigm, synthesizing AI with edge computing (EC) …
has emerged as a nascent computing paradigm, synthesizing AI with edge computing (EC) …
A general framework of motion planning for redundant robot manipulator based on deep reinforcement learning
Motion planning and its optimization is vital and difficult for redundant robot manipulator in
an environment with obstacles. In this article, a general motion planning framework that …
an environment with obstacles. In this article, a general motion planning framework that …
[HTML][HTML] Machine learning meets advanced robotic manipulation
Automated industries lead to high quality production, lower manufacturing cost and better
utilization of human resources. Robotic manipulator arms have major role in the automation …
utilization of human resources. Robotic manipulator arms have major role in the automation …
DHRL: a graph-based approach for long-horizon and sparse hierarchical reinforcement learning
Abstract Hierarchical Reinforcement Learning (HRL) has made notable progress in complex
control tasks by leveraging temporal abstraction. However, previous HRL algorithms often …
control tasks by leveraging temporal abstraction. However, previous HRL algorithms often …
Planning irregular object packing via hierarchical reinforcement learning
Object packing by autonomous robots is an important challenge in warehouses and logistics
industry. Most conventional data-driven packing planning approaches focus on regular …
industry. Most conventional data-driven packing planning approaches focus on regular …
Model-based reinforcement learning with probabilistic ensemble terminal critics for data-efficient control applications
This article proposes a data-efficient model-based reinforcement learning (RL) algorithm
empowered by reliable future reward estimates achieved through a confidence-based …
empowered by reliable future reward estimates achieved through a confidence-based …
Latent planning via expansive tree search
R Gieselmann, FT Pokorny - Advances in Neural …, 2022 - proceedings.neurips.cc
Planning enables autonomous agents to solve complex decision-making problems by
evaluating predictions of the future. However, classical planning algorithms often become …
evaluating predictions of the future. However, classical planning algorithms often become …
Imagination-augmented hierarchical reinforcement learning for safe and interactive autonomous driving in urban environments
Hierarchical reinforcement learning (HRL) incorporates temporal abstraction into
reinforcement learning (RL) by explicitly taking advantage of hierarchical structures. Modern …
reinforcement learning (RL) by explicitly taking advantage of hierarchical structures. Modern …
Learning Hierarchical Planning-Based Policies from Offline Data
Hierarchical policy architectures incorporating some planning component into the top-level
have shown superior performance and generalization in agent navigation tasks. Cost or …
have shown superior performance and generalization in agent navigation tasks. Cost or …