The ability to cooperate through language is a defining feature of humans. As the
perceptual, motory and planning capabilities of deep artificial networks increase …

Early research on physical human–robot interaction (pHRI) has necessarily focused on
device design—the creation of compliant and sensorized hardware, such as exoskeletons …

Problems of cooperation--in which agents seek ways to jointly improve their welfare--are
ubiquitous and important. They can be found at scales ranging from our daily routines--such …

The current mainstream approach to train natural language systems is to expose them to
large amounts of text. This passive learning is problematic if we are interested in developing …

Learning to communicate through interaction, rather than relying on explicit supervision, is
often considered a prerequisite for developing a general AI. We study a setting where two …

Recent progress in deep learning is essentially based on a “big data for small tasks”
paradigm, under which massive amounts of data are used to train a classifier for a single …

The ability of algorithms to evolve or learn (compositional) communication protocols has
traditionally been studied in the language evolution literature through the use of emergent …

Emergent communication aims for a better understanding of human language evolution and
building more efficient representations. We posit that reaching these goals will require …

Multi-agent reinforcement learning offers a way to study how communication could emerge
in communities of agents needing to solve specific problems. In this paper, we study the …

How do we know if communication is emerging in a multi-agent system? The vast majority of
recent papers on emergent communication show that adding a communication channel …