In particular, I study anticipation and its role in perception and collaboration, fluency between human and robotic team members, perceptual-symbol based cognition for robots. Other projects include hybrid-control puppeteering for robotic stage and film actors, and human-guided machine learning.
How can humans and robots perform live music together, in a fluent and synchronized way, even if the music is structured and precise? In this work I extend my anticipatory approach to time-critical human-robot musical performance. Shimon, a robotic marimba player listens to a human pianist, and plays it part in a a jazz duo.
Anticipatory Perceptual Simulation is a novel approach to robotic agent cognition, allowing a robot to adaptively train on a task in conjunction with a human teammate. During joint practice with a human, the robot increasingly anticipates the human's activity and uses it in form of perceptual simulation or "hallucination". We have implemented this framework on two robots, and - in human-subject studies - find the anticipatory behavior not only more efficient, balanced, and fluent, but that subjects find robots with this behavior more intelligent and contributing compared to a reactive robot.
Anticipation is a key ingredient in solitary action, and more importantly in joint action and collaboration. We have developed a generic anticipatory action framework for Markov Decision Processes, implemented it on a computer game agent, and show how anticipation improves efficiency, fluency, and trust when tested with untrained subjects.
AUR is a robotic desk lamp, a collaborative lighting assistant. It serves as a non-anthropomorphic robotic platform for studies of human-robot fluency, embodiment, and nonverbal behavior. AUR won the 2007 IEEE International Robot Design Competition.
We are developing the groundwork for robotic theater actors. These are built on principles from acting theory, and pave the way for fully autonomous robotic actors. We have developed a hybrid control system as a first stage, and staged a first-of-a-kind performance including two human and one robot actor.
Sophie's Kitchen is an online research platform in which untrained humans guide an agent that learns through reinforcement learning. We studied how people want to teach agents, and how reinforcement learning needs to change to fit human's expectation and tendencies.
A task planning and execution framework for an autonomous humanoid robot working shoulder-to-shoulder with
a human teammate, sharing the workspace and the objects required to complete a task. We implemented the
use of shared goals, dynamic meshing of sub-plans, and non-verbal behavior.
From a design perspective, there is a good reason to explore non-humanoid forms for personal robots. By steering away from imitating the human form, the design space opens up infinitely. By embellishing familiar objects — such as household items, tools, appliances, or furniture — we can encourage people to reconsider their relationships with the artifacts surrounding them.
this is the year 2010, but there's nothing copyrighted here.