11 research outputs found
A deep active inference model of the rubber-hand illusion
Understanding how perception and action deal with sensorimotor conflicts,
such as the rubber-hand illusion (RHI), is essential to understand how the body
adapts to uncertain situations. Recent results in humans have shown that the
RHI not only produces a change in the perceived arm location, but also causes
involuntary forces. Here, we describe a deep active inference agent in a
virtual environment, which we subjected to the RHI, that is able to account for
these results. We show that our model, which deals with visual high-dimensional
inputs, produces similar perceptual and force patterns to those found in
humans.Comment: 8 pages, 3 figures, Accepted in 1st International Workshop on Active
Inference, in Conjunction with European Conference of Machine Learning 2020.
The final authenticated publication is available online at
https://doi.org/10.1007/978-3-030-64919-7_1
Active Inference for Integrated State-Estimation, Control, and Learning
This work presents an approach for control, state-estimation and learning
model (hyper)parameters for robotic manipulators. It is based on the active
inference framework, prominent in computational neuroscience as a theory of the
brain, where behaviour arises from minimizing variational free-energy. The
robotic manipulator shows adaptive and robust behaviour compared to
state-of-the-art methods. Additionally, we show the exact relationship to
classic methods such as PID control. Finally, we show that by learning a
temporal parameter and model variances, our approach can deal with unmodelled
dynamics, damps oscillations, and is robust against disturbances and poor
initial parameters. The approach is validated on the `Franka Emika Panda' 7 DoF
manipulator.Comment: 7 pages, 6 figures, accepted for presentation at the International
Conference on Robotics and Automation (ICRA) 202
Deep Active Inference for Partially Observable MDPs
Deep active inference has been proposed as a scalable approach to perception
and action that deals with large policy and state spaces. However, current
models are limited to fully observable domains. In this paper, we describe a
deep active inference model that can learn successful policies directly from
high-dimensional sensory inputs. The deep learning architecture optimizes a
variant of the expected free energy and encodes the continuous state
representation by means of a variational autoencoder. We show, in the OpenAI
benchmark, that our approach has comparable or better performance than deep
Q-learning, a state-of-the-art deep reinforcement learning algorithm.Comment: 1st International Workshop on Active inference, European Conference
on Machine Learning (ECML/PCKDD 2020
Prediction error-driven memory consolidation for continual learning. On the case of adaptive greenhouse models
This work presents an adaptive architecture that performs online learning and
faces catastrophic forgetting issues by means of episodic memories and
prediction-error driven memory consolidation. In line with evidences from the
cognitive science and neuroscience, memories are retained depending on their
congruency with the prior knowledge stored in the system. This is estimated in
terms of prediction error resulting from a generative model. Moreover, this AI
system is transferred onto an innovative application in the horticulture
industry: the learning and transfer of greenhouse models. This work presents a
model trained on data recorded from research facilities and transferred to a
production greenhouse.Comment: Revised version. Paper under review, submitted to Springer German
Journal on Artificial Intelligence (K\"unstliche Intelligenz), Special Issue
on Developmental Robotic
Goal-Directed Planning for Habituated Agents by Active Inference Using a Variational Recurrent Neural Network
It is crucial to ask how agents can achieve goals by generating action plans
using only partial models of the world acquired through habituated
sensory-motor experiences. Although many existing robotics studies use a
forward model framework, there are generalization issues with high degrees of
freedom. The current study shows that the predictive coding (PC) and active
inference (AIF) frameworks, which employ a generative model, can develop better
generalization by learning a prior distribution in a low dimensional latent
state space representing probabilistic structures extracted from well
habituated sensory-motor trajectories. In our proposed model, learning is
carried out by inferring optimal latent variables as well as synaptic weights
for maximizing the evidence lower bound, while goal-directed planning is
accomplished by inferring latent variables for maximizing the estimated lower
bound. Our proposed model was evaluated with both simple and complex robotic
tasks in simulation, which demonstrated sufficient generalization in learning
with limited training data by setting an intermediate value for a
regularization coefficient. Furthermore, comparative simulation results show
that the proposed model outperforms a conventional forward model in
goal-directed planning, due to the learned prior confining the search of motor
plans within the range of habituated trajectories.Comment: 30 pages, 19 figure
End-to-End Pixel-Based Deep Active Inference for Body Perception and Action
We present a pixel-based deep active inference algorithm (PixelAI) inspired
by human body perception and action. Our algorithm combines the free-energy
principle from neuroscience, rooted in variational inference, with deep
convolutional decoders to scale the algorithm to directly deal with raw visual
input and provide online adaptive inference. Our approach is validated by
studying body perception and action in a simulated and a real Nao robot.
Results show that our approach allows the robot to perform 1) dynamical body
estimation of its arm using only monocular camera images and 2) autonomous
reaching to "imagined" arm poses in the visual space. This suggests that robot
and human body perception and action can be efficiently solved by viewing both
as an active inference problem guided by ongoing sensory input
Investigation of the Sense of Agency in Social Cognition, based on frameworks of Predictive Coding and Active Inference: A simulation study on multimodal imitative interaction
When agents interact socially with different intentions, conflicts are
difficult to avoid. Although how agents can resolve such problems autonomously
has not been determined, dynamic characteristics of agency may shed light on
underlying mechanisms. The current study focused on the sense of agency (SoA),
a specific aspect of agency referring to congruence between the agent's
intention in acting and the outcome. Employing predictive coding and active
inference as theoretical frameworks of perception and action generation, we
hypothesize that regulation of complexity in the evidence lower bound of an
agent's model should affect the strength of the agent's SoA and should have a
critical impact on social interactions. We built a computational model of
imitative interaction between a robot and a human via visuo-proprioceptive
sensation with a variational Bayes recurrent neural network, and simulated the
model in the form of pseudo-imitative interaction using recorded human body
movement data. A key feature of the model is that each modality's complexity
can be regulated differently with a hyperparameter assigned to each module. We
first searched for an optimal setting that endows the model with appropriate
coordination of multimodal sensation. This revealed that the vision module's
complexity should be more tightly regulated than that of the proprioception
module. Using the optimally trained model, we examined how changing the
tightness of complexity regulation after training affects the strength of the
SoA during interactions. The results showed that with looser regulation, an
agent tends to act more egocentrically, without adapting to the other. In
contrast, with tighter regulation, the agent tends to follow the other by
adjusting its intention. We conclude that the tightness of complexity
regulation crucially affects the strength of the SoA and the dynamics of
interactions between agents.Comment: 23 pages, 8 figure