Recent years have seen significant progress in the realm of robot autonomy, accompanied
by the expanding reach of robotic technologies. However, the emergence of new …

We propose a framework for planning in unknown dynamic environments with probabilistic
safety guarantees using conformal prediction. Particularly, we design a model predictive …

With the rapid developments in computational and communication resources, modern
industrial electronics systems can leverage networked structures to realize wireless remote …

The ability to accurately predict others' behavior is central to the safety and efficiency of
robotic systems in interactive settings, such as human–robot interaction and multi-robot …

Humans have internal models of robots (like their physical capabilities), the world (like what
will happen next), and their tasks (like a preferred goal). However, human internal models …

When robots interact with humans in homes, roads, or factories the human's behavior often
changes in response to the robot. Non-stationary humans are challenging for robot learners …

We present a multi-agent decision-making framework for the emergent coordination of
autonomous agents whose intents are initially undecided. Dynamic non-cooperative games …

The existence of representative datasets is a prerequisite of many successful artificial
intelligence and machine learning models. However, the subsequent application of these …

We propose an interaction-aware stochastic model predictive control (MPC) strategy for lane
merging tasks in automated driving. The MPC strategy is integrated with an online learning …

Game theoretic methods have become popular for planning and prediction in situations
involving rich multi-agent interactions. However, these methods often assume the existence …