Autonomous Driving requires high levels of coordination and collaboration between agents. Achieving effective coordination in multi-agent systems is a difficult task
that remains largely unresolved. Multi-Agent Reinforcement
Learning has arisen as a powerful method to accomplish
this task because it considers the interaction between agents
and also allows for decentralized training—which makes it
highly scalable. However, transferring policies from simulation
to the real world is a big challenge, even for single-agent
applications. Multi-agent systems add additional complexities to
the Sim-to-Real gap due to agent collaboration and environment
synchronization. In this paper, we propose a method to transfer
multi-agent autonomous driving policies to the real world. For
this, we create a multi-agent environment that imitates the
dynamics of the Duckietown multi-robot testbed, and train
multi-agent policies using the MAPPO algorithm with different
levels of domain randomization. We then transfer the trained
policies to the Duckietown testbed and show that when using
our method, domain randomization can reduce the reality gap
by 90%. Moreover, we show that different levels of parameter
randomization have a substantial impact on the Sim-to-Real gap.
Finally, our approach achieves significantly better results than
a rule-based benchmark.
Feature Extraction using Poincaré Plots for Gait Classification
and W. Erlhagen.
In 25th Portuguese Conference on Pattern Recognition (RECPAD)
The aim of this study is to evaluate different features, extracted from a
Poincaré plot of gait signals, in their ability to classify the gait of patients
with neurodegenerative diseases: Parkinson’s disease (PD) and Hunting-
ton’s disease (HD). Five different features that describe gait variability
were extracted from the Poincaré plots of two gait signals: stride time
and percentage of stride time spent in swing phase. Among the set of ex-
tracted features, those that displayed significant differences between the
two groups and were not correlated with each other, were used as input
to the support vector machine classifier. It was found that all extracted
features (with exception of one feature in PD vs healthy group compari-
son) are significantly different between healthy and pathological subjects
and are suitable to discriminate them (with accuracies greater than 80%).
When comparing PD vs HD, just three features were significantly differ-
ent, however, a relatively good classification accuracy (around 72%) was
achieved using two of them. The results demonstrate that it is feasible
to apply variability measures extracted from Poincaré plots of gait data
signals in gait classification problems.