  Autonomous agents, such as robots or intelligent devices, need to understand
how to interact with objects and its environment. Affordances are defined as
the relationships between an agent, the objects, and the possible future
actions in the environment. In this paper, we present a Bayesian deep learning
method to predict the affordances available in the environment directly from
RGB images. Based on previous work on socially accepted affordances, our model
is based on a multiscale CNN that combines local and global information from
the object and the full image. However, previous works assume a deterministic
model, but uncertainty quantification is fundamental for robust detection,
affordance-based reason, continual learning, etc. Our Bayesian model is able to
capture both the aleatoric uncertainty from the scene and the epistemic
uncertainty associated with the model and previous learning process. For
comparison, we estimate the uncertainty using two state-of-the-art techniques:
Monte Carlo dropout and deep ensembles. We also compare different types of CNN
encoders for feature extraction. We have performed several experiments on an
affordance database on socially acceptable behaviours and we have shown
improved performance compared with previous works. Furthermore, the uncertainty
estimation is consistent with the the type of objects and scenarios. Our
results show a marginal better performance of deep ensembles, compared to
MC-dropout on the Brier score and the Expected Calibration Error.
