  State-of-the-art sensorimotor learning algorithms offer policies that can
often produce unstable behaviors, damaging the robot and/or the environment.
Traditional robot learning, on the contrary, relies on dynamical system-based
policies that can be analyzed for stability/safety. Such policies, however, are
neither flexible nor generic and usually work only with proprioceptive sensor
states. In this work, we bridge the gap between generic neural network policies
and dynamical system-based policies, and we introduce Autonomous Neural Dynamic
Policies (ANDPs) that: (a) are based on autonomous dynamical systems, (b)
always produce asymptotically stable behaviors, and (c) are more flexible than
traditional stable dynamical system-based policies. ANDPs are fully
differentiable, flexible generic-policies that can be used in imitation
learning setups while ensuring asymptotic stability. In this paper, we explore
the flexibility and capacity of ANDPs in several imitation learning tasks
including experiments with image observations. The results show that ANDPs
combine the benefits of both neural network-based and dynamical system-based
methods.
