Noisy sensing, imperfect control, and environment changes are defining characteristics of
many real-world robot tasks. The partially observable Markov decision process (POMDP) …

We propose a decentralized variant of Monte Carlo tree search (MCTS) that is suitable for a
variety of tasks in multi-robot active perception. Our algorithm allows each robot to optimize …

The multiple-path orienteering problem asks for paths for a team of robots that maximize the
total reward collected while satisfying budget constraints on the path length. This problem …

Small uninhabited aerial vehicles (sUAVs) commonly rely on active propulsion to stay
airborne, which limits flight time and range. To address this, autonomous soaring seeks to …

Concurrent estimation and control of robotic systems remains an ongoing challenge, where
controllers rely on data extracted from states/parameters riddled with uncertainties and …

Planning under model uncertainty is a fundamental problem across many applications of
decision making and learning. In this paper, we propose the Robust Adaptive Monte Carlo …

Reinforcement learning is a research paradigm that is commonly utilized to tackle problems
involving sequential decision-making. Agents learn optimum policy from samples generated …

We propose a novel belief space planning technique for continuous dynamics by viewing
the belief system as a hybrid dynamical system with time-driven switching. Our approach is …

In this paper, we consider motion as a means of sending messages between robots. We
focus on a scenario in which a message is encoded in a sending robot's trajectory, and …

While reinforcement learning (RL) has made great advances in scalability, exploration and
partial observability are still active research topics. In contrast, Bayesian RL (BRL) provides …