Reinforcement learning is a promising approach for learning control policies for robot tasks.
However, specifying complex tasks (eg, with multiple objectives and safety constraints) can …

LTLf synthesis is the automated construction of a reactive system from a high-level
description, expressed in LTLf, of its finite-horizon behavior. So far, the conversion of LTLf …

Human-robot collaboration plays an important role in intelligent manufacturing. However,
the main challenge is how the robot can make online reactive changes to the plan based on …

We develop a sampling-based motion planning algorithm that combines long-term temporal
logic goals with short-term reactive requirements. The mission specification has two parts:(1) …

When humans and robots perform complex tasks together, the robot must have a strategy to
choose its actions based on observed human behavior. One well-studied approach for …

Many systems are naturally modeled as Markov Decision Processes (MDPs), combining
probabilities and strategic actions. Given a model of a system as an MDP and some logical …

The goal of programmatic Learning from Demonstration (LfD) is to learn a policy in a
programming language that can be used to control a robot's behavior from a set of user …

There is a growing interest on formal methods-based robotic planning for temporal logic
objectives. In this work, we extend the scope of existing synthesis methods to hyper …

Decomposition is a general principle in computational thinking, aiming at decomposing a
problem instance into easier subproblems. Indeed, decomposing a transition system into a …

As robots gain capabilities to enter our humancentric world, they require formalism and
algorithms that enable smart and efficient interactions. This is challenging, especially for …