Hello! I am Tristan Karch, a Ph.D. candidate at Inria in the Flowers team under the supervision of Pierre-Yves Oudeyer and Clément Moulin-Frier. I am working on Grounding Language Agents and Curiosity-driven exploration. I am studying how autonomous agents can be efficient learners in social contexts.
I have a diverse academic background ranging from Mechanical Engineering to Machine Learning. I started by completing a BSc and MSc at Ecole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland, where I specialized in Computational Fluid Dynamics (CFD) and Computational Science. I also followed a joint master’s degree program with the Institut Supérieur de l’Aéronautique et de l’Espace (ISAE-Supaero) in Toulouse, France and graduated from the Data and Decision Sciences progam.
After my studies I moved to New York to join the Innovation Lab of BNP Paribas, a major french bank. I was working on implementing state of the art Natural Language Processing models, adapting them to legal language.
Since october 2019, I joind the Flowers team at Inria Bordeaux as a PhD candidate under the supervision of Pierre-Yves Oudeyer and Clément Moulin-Frier.
Summary: Humans have an outstanding ability to teach and learn form each other without relying on pre-established communication protocol (not even expressing rewards). Could machine do the same? In this blog post we propose a new interactive learning paradigm to investigate this question.
Summary: This blog post presents a supra-communicative view of language and advocates for the use of language as a cognitive tool to organize the cognitive development of intrinsically motivated artificial agents. We go over studies revealing the cognitive functions of language in humans, cover similar uses of language in the design of artificial agents and advocate for the pursuit of Vygotskian embodied agents - artificial agents that leverage language as a cognitive tool to structure their continuous experience, form abstract representations, reason, imagine creative goals, plan towards them and simulate future possibilities.