Inference of Affordances and Active Motor Control in Simulated Agents
- URL: http://arxiv.org/abs/2202.11532v1
- Date: Wed, 23 Feb 2022 14:13:04 GMT
- Title: Inference of Affordances and Active Motor Control in Simulated Agents
- Authors: Fedor Scholz, Christian Gumbsch, Sebastian Otte, Martin V. Butz
- Abstract summary: We introduce an output-probabilistic, temporally predictive, modular artificial neural network architecture.
We show that our architecture develops latent states that can be interpreted as affordance maps.
In combination with active inference, we show that flexible, goal-directed behavior can be invoked.
- Score: 0.5161531917413706
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Flexible, goal-directed behavior is a fundamental aspect of human life. Based
on the free energy minimization principle, the theory of active inference
formalizes the generation of such behavior from a computational neuroscience
perspective. Based on the theory, we introduce an output-probabilistic,
temporally predictive, modular artificial neural network architecture, which
processes sensorimotor information, infers behavior-relevant aspects of its
world, and invokes highly flexible, goal-directed behavior. We show that our
architecture, which is trained end-to-end to minimize an approximation of free
energy, develops latent states that can be interpreted as affordance maps. That
is, the emerging latent states signal which actions lead to which effects
dependent on the local context. In combination with active inference, we show
that flexible, goal-directed behavior can be invoked, incorporating the
emerging affordance maps. As a result, our simulated agent flexibly steers
through continuous spaces, avoids collisions with obstacles, and prefers
pathways that lead to the goal with high certainty. Additionally, we show that
the learned agent is highly suitable for zero-shot generalization across
environments: After training the agent in a handful of fixed environments with
obstacles and other terrains affecting its behavior, it performs similarly well
in procedurally generated environments containing different amounts of
obstacles and terrains of various sizes at different locations. To improve and
focus model learning further, we plan to invoke active inference-based,
information-gain-oriented behavior also while learning the temporally
predictive model itself in the near future. Moreover, we intend to foster the
development of both deeper event-predictive abstractions and compact, habitual
behavioral primitives.
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