13 accepted papers
Planning under partial observability is an essential capability of autonomous robots. The Partially Observable Markov Decision Process (POMDP) provides a powerful framework for planning under partial observability problems, capturing the stochastic effects of actions and the limited information avai…
The aging and increasing complexity of infrastructures make efficient inspection planning more critical in ensuring safety. Thanks to sampling-based motion planning, many inspection planners are fast. However, they often require huge memory. This is particularly true when the structure under inspect
This paper proposes Partially Observable Reference Policy Programming, a novel anytime online approximate POMDP solver which samples meaningful future histories very deeply while simultaneously forcing a gradual policy update. We provide theoretical guarantees for the algorithm’s underlying scheme w
The Partially Observable Markov Decision Process (POMDP) provides a principled framework for decision making in stochastic partially observable environments. However, computing good solutions for problems with continuous action spaces remains challenging. To ease this challenge, we propose a simple…
Motion planning is a fundamental capability in robotics applications. Real-world scenarios can introduce uncertainty to the motion planning problem. In this work we study environment uncertainty in general high-dimensional problems wherein the choice of appropriate metrics and formulations are shown…
Efficiently searching for target objects in complex environments that contain various types of furniture, such as shelves, tables, and beds, is crucial for mobile robots, but it poses significant challenges due to various factors such as localization errors, limited field of view, and visual occlusi…
Many planning problems in robotics require long planning horizon and uncertain in nature. The Par-tially Observable Markov Descision Process (POMDP) is a mathematically principled framework for planning under uncertainty. To alleviate the difficulties of computing good approximate POMDP solutions fo…
Making good decisions in partially observable and non-deterministic scenarios is a crucial capability for robots. A Partially Observable Markov Decision Process (POMDP) is a general framework for the above problem. Despite advances in POMDP solving, problems with long planning horizons and evolving…
The ability to make good decisions in partially observed non-cooperative multi-agent scenarios is important for robots to interact effectively in human environments. A robust framework for such decision-making problems is the Interactive Partially Observable Markov Decision Processes (I-POMDPs), whi…
Planning under partial obervability is essential for autonomous robots. A principled way to address such planning problems is the Partially Observable Markov Decision Process (POMDP). Although solving POMDPs is computationally intractable, substantial advancements have been achieved in developing ap…
Service robots require the ability to recognize various household objects in order to carry out certain tasks, such as fetching an object for a person. Manually collecting information on all the objects a robot may encounter in a household is tedious and time-consuming; therefore this paper proposes…
Planning under partial observability is both challenging and critical for reliable robot operation. The past decade has seen substantial advances in this domain: The mathematically principled approach for addressing such problems, namely the Partially Observable Markov Decision Process (POMDP), has…
For agile, accurate autonomous robotics, it is desirable to plan motion in the presence of uncertainty. The Partially Observable Markov Decision Process (POMDP) provides a principled framework for this. Despite the tremendous advances of POMDP-based planning, most can only solve problems with a smal…