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

This paper presents INVIGORATE, a robot system that interacts with human through natural
language and grasps a specified object in clutter. The objects may occlude, obstruct, or even …

Robot planning in partially observable domains is difficult, because a robot needs to
estimate the current state and plan actions at the same time. When the domain includes …

In many reinforcement learning tasks, the agent has to learn to interact with many objects of
different types and generalize to unseen combinations and numbers of objects. Often a task …

Autonomous mobile robots employed in industrial applications often operate in complex and
uncertain environments. In this paper we propose an approach based on an extension of …

In realistic applications of object search, robots will need to locate target objects in complex
environments while coping with unreliable sensors, especially for small or hard-to-detect …

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 …

Searching for objects is a fundamental skill for robots. As such, we expect object search to
eventually become an off-the-shelf capability for robots, similar to eg, object detection and …

How can we build robots for open-world semantic navigation tasks, like searching for target
objects in novel scenes? While foundation models have the rich knowledge and …

In this paper, we present pomdp_py, a general purpose Partially Observable Markov
Decision Process (POMDP) library written in Python and Cython. Existing POMDP libraries …