79,247 research outputs found
Neural Network Based Reinforcement Learning for Audio-Visual Gaze Control in Human-Robot Interaction
This paper introduces a novel neural network-based reinforcement learning
approach for robot gaze control. Our approach enables a robot to learn and to
adapt its gaze control strategy for human-robot interaction neither with the
use of external sensors nor with human supervision. The robot learns to focus
its attention onto groups of people from its own audio-visual experiences,
independently of the number of people, of their positions and of their physical
appearances. In particular, we use a recurrent neural network architecture in
combination with Q-learning to find an optimal action-selection policy; we
pre-train the network using a simulated environment that mimics realistic
scenarios that involve speaking/silent participants, thus avoiding the need of
tedious sessions of a robot interacting with people. Our experimental
evaluation suggests that the proposed method is robust against parameter
estimation, i.e. the parameter values yielded by the method do not have a
decisive impact on the performance. The best results are obtained when both
audio and visual information is jointly used. Experiments with the Nao robot
indicate that our framework is a step forward towards the autonomous learning
of socially acceptable gaze behavior.Comment: Paper submitted to Pattern Recognition Letter
Deep Object-Centric Representations for Generalizable Robot Learning
Robotic manipulation in complex open-world scenarios requires both reliable
physical manipulation skills and effective and generalizable perception. In
this paper, we propose a method where general purpose pretrained visual models
serve as an object-centric prior for the perception system of a learned policy.
We devise an object-level attentional mechanism that can be used to determine
relevant objects from a few trajectories or demonstrations, and then
immediately incorporate those objects into a learned policy. A task-independent
meta-attention locates possible objects in the scene, and a task-specific
attention identifies which objects are predictive of the trajectories. The
scope of the task-specific attention is easily adjusted by showing
demonstrations with distractor objects or with diverse relevant objects. Our
results indicate that this approach exhibits good generalization across object
instances using very few samples, and can be used to learn a variety of
manipulation tasks using reinforcement learning
Attention-Aware Face Hallucination via Deep Reinforcement Learning
Face hallucination is a domain-specific super-resolution problem with the
goal to generate high-resolution (HR) faces from low-resolution (LR) input
images. In contrast to existing methods that often learn a single
patch-to-patch mapping from LR to HR images and are regardless of the
contextual interdependency between patches, we propose a novel Attention-aware
Face Hallucination (Attention-FH) framework which resorts to deep reinforcement
learning for sequentially discovering attended patches and then performing the
facial part enhancement by fully exploiting the global interdependency of the
image. Specifically, in each time step, the recurrent policy network is
proposed to dynamically specify a new attended region by incorporating what
happened in the past. The state (i.e., face hallucination result for the whole
image) can thus be exploited and updated by the local enhancement network on
the selected region. The Attention-FH approach jointly learns the recurrent
policy network and local enhancement network through maximizing the long-term
reward that reflects the hallucination performance over the whole image.
Therefore, our proposed Attention-FH is capable of adaptively personalizing an
optimal searching path for each face image according to its own characteristic.
Extensive experiments show our approach significantly surpasses the
state-of-the-arts on in-the-wild faces with large pose and illumination
variations
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