14,032 research outputs found
Learning to Role-Switch in Multi-Robot Systems
We present an approach that uses Q-learning on
individual robotic agents, for coordinating a mission-tasked
team of robots in a complex scenario. To reduce
the size of the state space, actions are grouped into sets of
related behaviors called roles and represented as
behavioral assemblages. A role is a Finite State Automata
such as Forager, where the behaviors and their
sequencing for finding objects, collecting them, and
returning them are already encoded and do not have to be
relearned. Each robot starts out with the same set of
possible roles to play, the same perceptual hardware for
coordination, and no contact other than perception
regarding other members of the team. Over the course of
training, a team of Q-learning robots will converge to
solutions that best the performance of a well-designed
handcrafted homogeneous team
SOTER: A Runtime Assurance Framework for Programming Safe Robotics Systems
The recent drive towards achieving greater autonomy and intelligence in
robotics has led to high levels of complexity. Autonomous robots increasingly
depend on third party off-the-shelf components and complex machine-learning
techniques. This trend makes it challenging to provide strong design-time
certification of correct operation.
To address these challenges, we present SOTER, a robotics programming
framework with two key components: (1) a programming language for implementing
and testing high-level reactive robotics software and (2) an integrated runtime
assurance (RTA) system that helps enable the use of uncertified components,
while still providing safety guarantees. SOTER provides language primitives to
declaratively construct a RTA module consisting of an advanced,
high-performance controller (uncertified), a safe, lower-performance controller
(certified), and the desired safety specification. The framework provides a
formal guarantee that a well-formed RTA module always satisfies the safety
specification, without completely sacrificing performance by using higher
performance uncertified components whenever safe. SOTER allows the complex
robotics software stack to be constructed as a composition of RTA modules,
where each uncertified component is protected using a RTA module.
To demonstrate the efficacy of our framework, we consider a real-world
case-study of building a safe drone surveillance system. Our experiments both
in simulation and on actual drones show that the SOTER-enabled RTA ensures the
safety of the system, including when untrusted third-party components have bugs
or deviate from the desired behavior
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