4,856 research outputs found
From motor babbling to hierarchical learning by imitation: a robot developmental pathway
How does an individual use the knowledge
acquired through self exploration as a manipulable model through which to understand
others and benefit from their knowledge?
How can developmental and social learning be
combined for their mutual benefit? In this
paper we review a hierarchical architecture
(HAMMER) which allows a principled way
for combining knowledge through exploration
and knowledge from others, through the creation and use of multiple inverse and forward
models. We describe how Bayesian Belief Networks can be used to learn the association
between a robot’s motor commands and sensory consequences (forward models), and how
the inverse association can be used for imitation. Inverse models created through self
exploration, as well as those from observing
others can coexist and compete in a principled unified framework, that utilises the simulation theory of mind approach to mentally
rehearse and understand the actions of others
Developmental learning of internal models for robotics
Abstract: Robots that operate in human environments can learn motor skills asocially, from selfexploration, or socially, from imitating their peers. A robot capable of doing both can be more ~daptiveand autonomous. Learning by imitation, however, requires the ability to understand the actions ofothers in terms ofyour own motor system: this information can come from a robot's own exploration. This thesis investigates the minimal requirements for a robotic system than learns from both self-exploration and imitation of others. .Through self.exploration and computer vision techniques, a robot can develop forward 'models: internal mo'dels of its own motor system that enable it to predict the consequences of its actions. Multiple forward models are learnt that give the robot a distributed, causal representation of its motor system. It is demon~trated how a controlled increase in the complexity of these forward models speeds up the robot's learning. The robot can determine the uncertainty of its forward models, enabling it to explore so as to improve the accuracy of its???????predictions. Paying attention fO the forward models according to how their uncertainty is changing leads to a development in the robot's exploration: its interventions focus on increasingly difficult situations, adapting to the complexity of its motor system. A robot can invert forward models, creating inverse models, in order to estimate the actions that will achieve a desired goal. Switching to socialleaming. the robot uses these inverse model~ to imitate both a demonstrator's gestures and the underlying goals of their movement.Imperial Users onl
RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement Learning
Whole-slide images (WSI) in computational pathology have high resolution with
gigapixel size, but are generally with sparse regions of interest, which leads
to weak diagnostic relevance and data inefficiency for each area in the slide.
Most of the existing methods rely on a multiple instance learning framework
that requires densely sampling local patches at high magnification. The
limitation is evident in the application stage as the heavy computation for
extracting patch-level features is inevitable. In this paper, we develop
RLogist, a benchmarking deep reinforcement learning (DRL) method for fast
observation strategy on WSIs. Imitating the diagnostic logic of human
pathologists, our RL agent learns how to find regions of observation value and
obtain representative features across multiple resolution levels, without
having to analyze each part of the WSI at the high magnification. We benchmark
our method on two whole-slide level classification tasks, including detection
of metastases in WSIs of lymph node sections, and subtyping of lung cancer.
Experimental results demonstrate that RLogist achieves competitive
classification performance compared to typical multiple instance learning
algorithms, while having a significantly short observation path. In addition,
the observation path given by RLogist provides good decision-making
interpretability, and its ability of reading path navigation can potentially be
used by pathologists for educational/assistive purposes. Our code is available
at: \url{https://github.com/tencent-ailab/RLogist}.Comment: accepted by AAAI 202
A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization
Inspired by the great success of machine learning (ML), researchers have
applied ML techniques to visualizations to achieve a better design,
development, and evaluation of visualizations. This branch of studies, known as
ML4VIS, is gaining increasing research attention in recent years. To
successfully adapt ML techniques for visualizations, a structured understanding
of the integration of ML4VISis needed. In this paper, we systematically survey
88 ML4VIS studies, aiming to answer two motivating questions: "what
visualization processes can be assisted by ML?" and "how ML techniques can be
used to solve visualization problems?" This survey reveals seven main processes
where the employment of ML techniques can benefit visualizations:Data
Processing4VIS, Data-VIS Mapping, InsightCommunication, Style Imitation, VIS
Interaction, VIS Reading, and User Profiling. The seven processes are related
to existing visualization theoretical models in an ML4VIS pipeline, aiming to
illuminate the role of ML-assisted visualization in general
visualizations.Meanwhile, the seven processes are mapped into main learning
tasks in ML to align the capabilities of ML with the needs in visualization.
Current practices and future opportunities of ML4VIS are discussed in the
context of the ML4VIS pipeline and the ML-VIS mapping. While more studies are
still needed in the area of ML4VIS, we hope this paper can provide a
stepping-stone for future exploration. A web-based interactive browser of this
survey is available at https://ml4vis.github.ioComment: 19 pages, 12 figures, 4 table
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