Research

Reasoning and Planning for Mobile Robots

To be deemed intelligent in human-robot coexisting environments, robots need the capability of representing and reasoning with commonsense knowledge. Such knowledge is normally true, but not always. For instance, people prefer coffee in the morning; and office doors are closed during the holidays. We use answer set programming (ASP) and its extensions for commonsense reasoning and partially observable Markov decision processes (POMDPs) for probabilistic planning. The goal of this research is to develop algorithms to enable robots to plan under uncertainty while reason with commonsense knowledge at the same time.

Representative Publications:
CORPP: Commonsense Reasoning and Probabilistic Planning, as Applied to Dialog with a Mobile Robot. (AAAI 2015)

Multirobot Symbolic Planning under Temporal Uncertainty (AAMAS 2017)

Dynamically Constructed (PO)MDPs for Adaptive Robot Planning (AAAI 2017)

Integrated Task and Motion Planning for Mobile Service Robots (RSS 2016)

Mobile Robot Planning using Action Language BC with an Abstraction Hierarchy (LPNMR 2015)

Planning in Answer Set Programming while Learning Action Costs for Mobile Robots (AAAI-SSS 2014)

Human-Robot Interaction

Robots are becoming increasingly sophisticated, and are bound to become pervasive in humans’ every-day lives. To effectively collaborate with humans, it is useful for a robot to understand their activities and intentions automatically. This understanding is especially important in human-robot interaction scenarios: if the robot can properly interpret the behavior of humans, its communication with them will be facilitated, and its ability to interact with them will improve. Therefore, the aim of this research is to develop new methods that would allow a robot to effectively recognize human activities, intentions, etc. and appropriately react to them.

Representative Publications:

BWIBots: A platform for bridging the gap between AI and Human-Robot Interaction research (The International Journal of Robotics Research 2017)

Robot-centric activity recognition ‘in the wild’. (ICSR 2015)

Multi-Robot Coordination and Guidance

In this research, we demonstrate how individual service robots in a multi-robot system can be temporarily reassigned from their original task to help guide a human from one location to another in the environment. We formulate this multi-robot treatment of the human guidance problem as a Markov Decision Process (MDP), and explore how different MDP planners can be used to solve this problem. Our long term goal for this research is to expand the MDP to a general framework for efficiently interrupting robots performing background service tasks to efficiently aid humans as necessary.

Representative Publications:

Multi-Robot Human Guidance: Human Experiments and Multiple Concurrent Requests (AAMAS 2017)

Grounded Language Learning

Humans use natural language to articulate their thoughts and intentions to other people, making it a natural channel for human-robot communication. We demonstrate methods for parsing natural language to underlying meanings, and using robotic sensors to create multi-modal models of perceptual concepts (e.g. ``Bring the heavy mug to Bob's office.''). Our robots use dialogs with humans to improve both parsing and perception. Our long-term goal with this research is to enable lifelong learning of new language constructions and perceptual concept models while accomplishing tasks. We have implemented a language learning agent on a Segbot V2 robot equipped with a Kinova arm, thus incorporating speech, perception, navigation and manipulation in a single autonomous robot.

A demo of this system’s abilities can be viewed here

Representative Publications:

Improving Grounded Natural Language Understanding through Human-Robot Dialog (ICRA 2019)