Learning Manipulation Skills Via Hierarchical Spatial Attention

by   Marcus Gualtieri, et al.
Northeastern University

Learning generalizable skills in robotic manipulation has long been challenging due to real-world sized observation and action spaces. One method for addressing this problem is attention focus -- the robot learns where to attend its sensors and irrelevant details are ignored. However, these methods have largely not caught on due to the difficulty of learning a good attention policy and the added partial observability induced by a narrowed window of focus. This article addresses the first issue by constraining gazes to a spatial hierarchy. For the second issue, we identify a case where the partial observability induced by attention does not prevent Q-learning from finding an optimal policy. We conclude with real-robot experiments on challenging pick-place tasks demonstrating the applicability of the approach.


page 9

page 11

page 13

page 15


Learning Failure Prevention Skills for Safe Robot Manipulation

Robots are more capable of achieving manipulation tasks for everyday act...

Learning to Scaffold the Development of Robotic Manipulation Skills

Learning contact-rich, robotic manipulation skills is a challenging prob...

Efficiently Learning Recoveries from Failures Under Partial Observability

Operating under real world conditions is challenging due to the possibil...

Point Cloud Based Reinforcement Learning for Sim-to-Real and Partial Observability in Visual Navigation

Reinforcement Learning (RL), among other learning-based methods, represe...

Learning 6-DoF Grasping and Pick-Place Using Attention Focus

We address a class of manipulation problems where the robot perceives th...

Learning a Generative Transition Model for Uncertainty-Aware Robotic Manipulation

Robot learning of real-world manipulation tasks remains challenging and ...

Equivariant Q Learning in Spatial Action Spaces

Recently, a variety of new equivariant neural network model architecture...

Please sign up or login with your details

Forgot password? Click here to reset