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Review of deep reinforcement learning-based object gras**: 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 …
Sim-to-real transfer of robotic control with dynamics randomization
Simulations are attractive environments for training agents as they provide an abundant
source of data and alleviate certain safety concerns during the training process. But the …
source of data and alleviate certain safety concerns during the training process. But the …
Deep drone racing: From simulation to reality with domain randomization
Dynamically changing environments, unreliable state estimation, and operation under
severe resource constraints are fundamental challenges that limit the deployment of small …
severe resource constraints are fundamental challenges that limit the deployment of small …
Driving policy transfer via modularity and abstraction
M Müller, A Dosovitskiy, B Ghanem, V Koltun - ar**, perception and interaction: A survey
For robots to navigate and interact more richly with the world around them, they will likely
require a deeper understanding of the world in which they operate. In robotics and related …
require a deeper understanding of the world in which they operate. In robotics and related …
Transfer in deep reinforcement learning using successor features and generalised policy improvement
The ability to transfer skills across tasks has the potential to scale up reinforcement learning
(RL) agents to environments currently out of reach. Recently, a framework based on two …
(RL) agents to environments currently out of reach. Recently, a framework based on two …
An adaptive deep reinforcement learning framework enables curling robots with human-like performance in real-world conditions
The game of curling can be considered a good test bed for studying the interaction between
artificial intelligence systems and the real world. In curling, the environmental characteristics …
artificial intelligence systems and the real world. In curling, the environmental characteristics …
Modular networks: Learning to decompose neural computation
Scaling model capacity has been vital in the success of deep learning. For a typical network,
necessary compute resources and training time grow dramatically with model size …
necessary compute resources and training time grow dramatically with model size …
Bev-seg: Bird's eye view semantic segmentation using geometry and semantic point cloud
Bird's-eye-view (BEV) is a powerful and widely adopted representation for road scenes that
captures surrounding objects and their spatial locations, along with overall context in the …
captures surrounding objects and their spatial locations, along with overall context in the …
Pose-and-shear-based tactile servoing
Tactile servoing is an important technique because it enables robots to manipulate objects
with precision and accuracy while adapting to changes in their environments in real-time …
with precision and accuracy while adapting to changes in their environments in real-time …