  When deploying autonomous agents in unstructured environments over sustained
periods of time, adaptability and robustness oftentimes outweigh optimality as
a primary consideration. In other words, safety and survivability constraints
play a key role and in this paper, we present a novel, constraint-learning
framework for control tasks built on the idea of constraints-driven control.
However, since control policies that keep a dynamical agent within state
constraints over infinite horizons are not always available, this work instead
considers constraints that can be satisfied over some finite time horizon T >
0, which we refer to as limited-duration safety. Consequently, value function
learning can be used as a tool to help us find limited-duration safe policies.
We show that, in some applications, the existence of limited-duration safe
policies is actually sufficient for long-duration autonomy. This idea is
illustrated on a swarm of simulated robots that are tasked with covering a
given area, but that sporadically need to abandon this task to charge
batteries. We show how the battery-charging behavior naturally emerges as a
result of the constraints. Additionally, using a cart-pole simulation
environment, we show how a control policy can be efficiently transferred from
the source task, balancing the pole, to the target task, moving the cart to one
direction without letting the pole fall down.
