Review of deep reinforcement learning-based object grasping: Techniques, open challenges, and recommendations
MQ Mohammed, KL Chung, CS Chyi - IEEE Access, 2020 - ieeexplore.ieee.org
The motivation behind our work is to review and analyze the most relevant studies on deep
reinforcement learning-based object manipulation. Various studies are examined through a …
reinforcement learning-based object manipulation. Various studies are examined through a …
Goal-conditioned reinforcement learning: Problems and solutions
Goal-conditioned reinforcement learning (GCRL), related to a set of complex RL problems,
trains an agent to achieve different goals under particular scenarios. Compared to the …
trains an agent to achieve different goals under particular scenarios. Compared to the …
Language conditioned imitation learning over unstructured data
C Lynch, P Sermanet - arXiv preprint arXiv:2005.07648, 2020 - arxiv.org
Natural language is perhaps the most flexible and intuitive way for humans to communicate
tasks to a robot. Prior work in imitation learning typically requires each task be specified with …
tasks to a robot. Prior work in imitation learning typically requires each task be specified with …
Gnm: A general navigation model to drive any robot
Learning provides a powerful tool for vision-based navigation, but the capabilities of
learning-based policies are constrained by limited training data. If we could combine data …
learning-based policies are constrained by limited training data. If we could combine data …
Language-conditioned learning for robotic manipulation: A survey
Language-conditioned robotic manipulation represents a cutting-edge area of research,
enabling seamless communication and cooperation between humans and robotic agents …
enabling seamless communication and cooperation between humans and robotic agents …
Autotelic agents with intrinsically motivated goal-conditioned reinforcement learning: a short survey
Building autonomous machines that can explore open-ended environments, discover
possible interactions and build repertoires of skills is a general objective of artificial …
possible interactions and build repertoires of skills is a general objective of artificial …
Language as a cognitive tool to imagine goals in curiosity driven exploration
Developmental machine learning studies how artificial agents can model the way children
learn open-ended repertoires of skills. Such agents need to create and represent goals …
learn open-ended repertoires of skills. Such agents need to create and represent goals …
Hierarchical reinforcement learning with universal policies for multistep robotic manipulation
Multistep tasks, such as block stacking or parts (dis) assembly, are complex for autonomous
robotic manipulation. A robotic system for such tasks would need to hierarchically combine …
robotic manipulation. A robotic system for such tasks would need to hierarchically combine …
Goal-aware prediction: Learning to model what matters
Learned dynamics models combined with both planning and policy learning algorithms
have shown promise in enabling artificial agents to learn to perform many diverse tasks with …
have shown promise in enabling artificial agents to learn to perform many diverse tasks with …
What is essential for unseen goal generalization of offline goal-conditioned rl?
Offline goal-conditioned RL (GCRL) offers a way to train general-purpose agents from fully
offline datasets. In addition to being conservative within the dataset, the generalization …
offline datasets. In addition to being conservative within the dataset, the generalization …