2,546 research outputs found
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
This paper was motivated by the problem of how to make robots fuse and
transfer their experience so that they can effectively use prior knowledge and
quickly adapt to new environments. To address the problem, we present a
learning architecture for navigation in cloud robotic systems: Lifelong
Federated Reinforcement Learning (LFRL). In the work, We propose a knowledge
fusion algorithm for upgrading a shared model deployed on the cloud. Then,
effective transfer learning methods in LFRL are introduced. LFRL is consistent
with human cognitive science and fits well in cloud robotic systems.
Experiments show that LFRL greatly improves the efficiency of reinforcement
learning for robot navigation. The cloud robotic system deployment also shows
that LFRL is capable of fusing prior knowledge. In addition, we release a cloud
robotic navigation-learning website based on LFRL
A Universal Semantic-Geometric Representation for Robotic Manipulation
Robots rely heavily on sensors, especially RGB and depth cameras, to perceive
and interact with the world. RGB cameras record 2D images with rich semantic
information while missing precise spatial information. On the other side, depth
cameras offer critical 3D geometry data but capture limited semantics.
Therefore, integrating both modalities is crucial for learning representations
for robotic perception and control. However, current research predominantly
focuses on only one of these modalities, neglecting the benefits of
incorporating both. To this end, we present Semantic-Geometric Representation
(SGR), a universal perception module for robotics that leverages the rich
semantic information of large-scale pre-trained 2D models and inherits the
merits of 3D spatial reasoning. Our experiments demonstrate that SGR empowers
the agent to successfully complete a diverse range of simulated and real-world
robotic manipulation tasks, outperforming state-of-the-art methods
significantly in both single-task and multi-task settings. Furthermore, SGR
possesses the unique capability to generalize to novel semantic attributes,
setting it apart from the other methods
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