Origin of the project
The Chroma group was created in the beginning of year 2015 (March). It regroups researchers who address perception and decision-making issues in mobile robotics and who share common approaches that mainly relates to the field of artificial intelligence. The group is gathering some members of the previous eMotion Inria project-team led by Christian Laugier (2002-2014) and of teacher-researchers from INSA
The Chroma group was initially composed of Olivier Simonin (Prof. INSA Lyon), Christian Laugier (Inria researcher DR1), Jilles Dibangoye (Asso. Prof. INSA Lyon), Agostino Martinelli (Inria researcher CR1) and Dizan Vasquez (Inria starting researcher SRP). On December 1, 2015, Anne Spalanzani (Asso. Prof. Univ. Grenoble, habilite) has joined the group (she was previously in Prima and eMotion Inria teams). In January 2016, Dizan Vasquez has left the group to join the Apple company.
The overall objective of Chroma team is to address fundamental and open issues that lie at the intersection of the emerging research fields called “Human Centered Robotics”
More precisely, our goal is to design algorithms and develop models allowing mobile robots to navigate and cooperate in dynamic and human-populated environments. Chroma is involved in all decision aspects pertaining to single and multi robot navigation tasks, including perception and motion-planning.
The general objective is to build robotic behaviors that allow one or several robots to operate safely among humans in partially known environments, where time, dynamics and interactions play a major role. Recent advances on embedded computational power, on sensor and communication technologies, and on miniaturized mechatronic systems, make the required technological breakthroughs possible (including from the scalability point of view).
Chroma is clearly positioned in the third challenge of the Inria 2013-2017 Strategic Plan “Interacting with the real and digital worlds: interaction, uses and learning”.
Our approach for addressing the previous challenge is to bring together probabilistic methods, planning techniques and multi-agent decision models. This will be done in cooperation with other disciplines such as sociology for the purpose of taking into account human models. Two main research themes of mobile robotics are addressed : i) Perception and situation awareness ii) Navigation and Cooperation in Dynamic Environments. Next, we elaborate more about these two research axes.
Perception and Situation Awareness. The main problem is to understand complex dynamic scenes involving mobile objects and human beings, by exploiting prior knowledge and a stream of perceptual data coming from various sensors. Our approach for solving this problem is to develop three complementary problem domains:
Bayesian Perception: How to take into account prior knowledge and uncertain sensory data in a dynamic context?
Situation awareness : How to interpret the perceived scene and to predict their likely future motion (including near future collision risk) ?
Robust state estimation: acquire a deep understanding on several sensor fusion problems and investigate their observability properties in the case of unknown inputs.
Navigation and Cooperation in Dynamic Environments. The challenge is to build models allowing robots to move and to coordinate efficiently in dynamic environments. We focus on two problems : navigation in human-populated environment (social navigation) and cooperation in large distributed fleet of robots (scalability and robustness issues).
Motion-planning in human-populated environment. How to plan trajectories that take into account the uncertainty of human-populated environments and that can respect the social rules of humans ? Such a challenge requires human behavior models, or to learn them, and planning algorithms that take into account them.
Decision Making in Multi-robot systems. The goal of this axis is to develop models and algorithms that provide both scalability and performance guarantees in real-world robotic systems. Our methodology builds upon complementary advantages of two orthogonal approaches, Multi-Agent Sequential Decision Making (MA-SDM) and Swarm Intelligence (SI).
The Chroma project is also concerned with applications and transfer of the scientific results. Our main application domains concern autonomous connected vehicles and service robotics. They are presented in Sections and . Chroma have currently projects developed with industrial (as Renault and Toyota) and startup partners.