Origin of the project
Chroma is a bi-localized project-team at Inria Lyon and Inria Grenoble (in Auvergne-Rhône-Alpes region). The project was launched in 2015 before it became officially an Inria project-team on December 1st, 2017. It brings together experts in perception and decision-making for mobile robotics and intelligent transport, all of them sharing common approaches that mainly relate to the field of Artificial Intelligence. It was originally founded by members of the working group on robotics at CITI lab1, led by Prof. Olivier Simonin (INSA Lyon2), and members from Inria project-team eMotion (2002-2014), led by Christian Laugier, at Inria Grenoble. Earlier members include Olivier Simonin (Prof. INSA Lyon), Christian Laugier (Inria researcher DR, Grenoble), Anne Spalanzani (Prof., UGA), Jilles Dibangoye (Asso. Prof. INSA Lyon) and Agostino Martinelli (Inria researcher CR, Grenoble). On January 2020, Christine Solnon (Prof. INSA Lyon) joined the team, thanks to her transfer from LIRIS lab. to CITI lab. On October 2021, Alessandro Renzaglia (Inria researcher CR, Lyon) was recruited through the Inria researcher recruitment campaign.
The overall objective of Chroma is to address fundamental and open issues that lie at the intersection of the emerging research fields called “Human Centered Robotics” 3, “Multi-Robot Systems” 4, and AI for humanity.
More precisely, our goal is to design algorithms and models that allow autonomous agents to perceive, decide, learn, and finally adapt to their environment. A focus is given to unknown and human-populated environments, where robots or vehicles have to navigate and cooperate to fulfill complex tasks.
In this context, recent advances in embedded computational power, sensor and communication technologies, and miniaturized mechatronic systems, make the required technological breakthroughs possible.
Chroma is clearly positioned in the “Artificial Intelligence and Autonomous systems” research theme of the
Inria 2018-2022 Strategic Plan. More specifically we refer to the “Augmented Intelligence” challenge (connected autonomous vehicles) and to the “Human centred digital world” challenge (interactive adaptation).
To address the mentioned challenges, we take advantage of recent advances in:
probabilistic methods, machine learning, planning techniques, multi-agent decision making, and constrained optimisation tools. We also draw inspiration from other disciplines such as Sociology, to take into account human models, or Physics/Biology, to study self-organized systems.
Chroma research is organized in two main axes : i) Perception and Situation Awareness ii) Decision Making. Next, we elaborate more about these axes.
Perception and Situation Awareness.This theme aims at understanding complex dynamic scenes, involving mobile objects and human beings, by exploiting prior knowledge and streams of perceptual data coming from various sensors. To this end, we investigate three complementary research problems: Bayesian & AI based Perception: How to interpret in real-time a complex dynamic scene perceived using a set of different sensors, and how to predict the near future evolution of this dynamic scene and the related collision risks ? How to extract the semantic information and to process it for the autonomous navigation step. Modeling and simulation of dynamic environments: How to model or learn the behavior of dynamic agents (pedestrians, cars, cyclists…) in order to better anticipate their trajectories? Robust state estimation: Acquire a deep understanding on several sensor fusion problems and investigate their observability properties in the case of unknown inputs.
Decision making.This theme aims to design algorithms and architectures that can achieve both scalability and quality for decision making in intelligent robotic systems and more generally for problem solving. Our methodology builds upon advantages of three (complementary) approaches: online planning, machine learning, and NP-hard optimization problem solving. Online planning: In this theme we study planning algorithms for single and fleet of cooperative mobile robots when they face complex and dynamics environments, i.e. populated by humans and/or mostly unknown. Machine learning: We search for structural properties–e.g., uniform continuity–which enable us to design efficient planning and (deep) reinforcement learning methods to solving complex single or multi-agent decision-making tasks. Offline constrained optimisation problem: We design and study approaches based on Constraint Programming (CP) and meta-heuristics to solve NP-hard problems such as planning and routing problems, for example.
Chroma is also concerned with applications and transfer of the scientific results. Our main applications include autonomous and connected vehicles, service robotics, exploration & mapping tasks with ground and aerial robots. Chroma is currently involved in several projects in collaboration with automobile companies (Renault, Toyota) and some startups (see Section 4).
The team has its own robotic platforms to support the experimentation activity (see5). In Grenoble, we have two experimental vehicles equipped with various sensors: a Toyota Lexus and a Renault Zoe; the Zoe car has been automated in December 2016. We have also developed two experimental test tracks respectively at Inria Grenoble (including connected roadside sensors and a controlled dummy pedestrian) and at IRT Nanoelec & CEA Grenoble (including a road intersection with traffic lights and several urban road equipments). In Lyon, we have a fleet of UAVs (Unmanned Aerial Vehicles) composed of 4 PX4 Vision, 2 IntelAero and 5 mini-UAVs Crazyflies. We have also a fleet of ground robots composed of 16 Turtlebot and 3 humanoids Pepper. The platforms are maintained and developed by contractual engineers, and by Lukas Rummelhard, from SED, in Grenoble.