Maxime Petit

Maxime Petit
Ecole Centrale de Lyon | ECL · Département Mathématiques - Informatique - M.I.

PhD

About

29
Publications
7,056
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
559
Citations
Introduction
Additional affiliations
January 2017 - present
Ecole Centrale de Lyon
Position
  • PostDoc Position
Description
  • Reinforcement Deep Learning for Intelligent and Adaptive Picking/Kitting with a Robotic Platform
June 2014 - December 2016
Imperial College London
Position
  • Postdotoral Research Associate
Description
  • www.imperial.ac.uk/personalrobotics
September 2011 - March 2012
Institut National des Sciences Appliquées de Lyon
Position
  • Lecturer
Description
  • * Computer Architecture * UNIX and tools * Database and SQL
Education
September 2009 - September 2010
Université Paris-Sud 11
Field of study
  • Computer Science
September 2007 - September 2010
Institut National des Sciences Appliquées de Lyon
Field of study
  • Biosciences - Bio-Informatics and Modeling
September 2005 - September 2007
Université Clermont Auvergne
Field of study
  • Biology

Publications

Publications (29)
Preprint
Full-text available
In robotics, methods and softwares usually require optimizations of hyperparameters in order to be efficient for specific tasks, for instance industrial bin-picking from homogeneous heaps of different objects. We present a developmental framework based on long-term memory and reasoning modules (Bayesian Optimisation, visual similarity and parameter...
Preprint
We propose a procedural fruit tree rendering framework, based on Blender and Python scripts allowing to generate quickly labeled dataset (i.e. including ground truth semantic segmentation). It is designed to train image analysis deep learning methods (e.g. in a robotic fruit harvesting context), where real labeled training datasets are usually scar...
Conference Paper
Full-text available
We propose a procedural fruit tree rendering framework, based on Blender and Python scripts allowing to generate quickly labeled dataset (i.e. including ground truth semantic segmentation). It is designed to train image analysis deep learning methods (e.g. in a robotic fruit harvesting context), where real labeled training datasets are usually scar...
Article
Full-text available
This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine,...
Article
Full-text available
We present a novel framework for finding the kinematic structure correspondences between two articulated objects in videos via hypergraph matching. In contrast to appearance and graph alignment based matching methods, which have been applied among two similar static images, the proposed method finds correspondences between two dynamic kinematic str...
Conference Paper
Full-text available
We present a developmental framework based on a long-term memory and reasoning mechanisms (Vision Similarity and Bayesian Optimisation). This architecture allows a robot to optimize autonomously hyper-parameters that need to be tuned from any action and/or vision module, treated as a black-box. The learning can take advantage of past experiences (s...
Article
Full-text available
The development of reasoning systems exploiting expert knowledge from interactions with humans is a non-trivial problem, particularly when considering how the information can be coded in the knowledge representation. For example, in human development, the acquisition of knowledge at one level requires the consolidation of knowledge from lower level...
Conference Paper
Full-text available
Various research topics are emerging as the demand for intelligent lifelong interactions between robot and humans increases. Among them, we can find the examination of persistent storage, the continuous unsupervised annotation of memories and the usage of data at high-frequency over long periods of time. We recently proposed a lifelong autobiograph...
Conference Paper
Full-text available
In the future, robots will support humans in their every day activities. One particular challenge that robots will face is understanding and reasoning about the actions of other agents in order to cooperate effectively with humans. We propose to tackle this using a developmental framework, where the robot incrementally acquires knowledge, and in pa...
Article
Full-text available
Robot systems that interact with humans over extended periods of time will benefit from storing and recalling large amounts of accumulated sensorimotor and interaction data. We provide a principled framework for the cumulative organisation of streaming autobiographical data so that data can be continuously processed and augmented as the processing...
Conference Paper
Full-text available
In this paper, we present a novel framework for finding the kinematic structure correspondence between two objects in videos via hypergraph matching. In contrast to prior appearance and graph alignment based matching methods which have been applied among two similar static images, the proposed method finds correspondences between two dynamic kinema...
Conference Paper
Full-text available
How humans acquire language, and in particular two or more different languages with the same neural computing substrate, is still an open issue. To address this issue we suggest to build models that are able to process any language from the very beginning. Here we propose a developmental and neuro-inspired approach that processes sentences word by...
Conference Paper
Full-text available
We present here a system capable of learning to extract the correct comprehension and production of personal pronouns and proper nouns during Human-Robot or Human-Human interactions. We use external 3D spatial and acoustic sensors with the robot iCub to allow the system to learn the proper mapping between different pronouns and names to their prope...
Article
Full-text available
A developing cognitive system will ideally acquire knowledge of its interaction in the world, and will be able to use that knowledge to construct a scaffolding for progressively structured levels of behavior. The current research implements and tests an autobiographical memory system by which a humanoid robot, the iCub, can accumulate its experienc...
Article
Full-text available
One of the principal functions of human language is to allow people to coordinate joint action. This includes the description of events, requests for action, and their organization in time. A crucial component of language acquisition is learning the grammatical structures that allow the expression of such complex meaning related to physical events....
Article
Full-text available
From automata to robots, the Human has always been fascinated by machines which could execute tasks for him, in several domains like industry or services. Indeed, we have used a developmental approach, where the robot has to learn new tasks during his life. Inspired by theories in child development, we have extracted the interesting concepts to imp...
Conference Paper
A new research platform has been developed to study human-robot interaction and communication. In this setup, a humanoid robot is used as a proxy between two humans involved in dyadic interactions. An experimenter is bound with a humanoid robot. He can control in real-time and sensor free the eye and face/head movements performed by a humanoid robo...
Conference Paper
Full-text available
Cooperation1 is at the core of human social life. In this context, two major challenges face research on humanrobot interaction: the first is to understand the underlying structure of cooperation, and the second is to build, based on this understanding, artificial agents that can successfully and safely interact with humans. Here we take a psycholo...
Conference Paper
Full-text available
In order to be able to understand a conversation in interaction, a robot, has to first understand the language used by his interlocutor. A central aspect of language learning is adaptability. Individuals can learn new words and new grammatical structures. We have developed learning methods that allow the humanoid robot iCub to robot can learn new l...
Conference Paper
Full-text available
The ability to generate and exploit internal models of the body, the environment, and their interaction is crucial for survival. Referred to as a forward model, this simulation capability plays an important role in motor control. In this context, the motor command is sent to the forward model in parallel with its actual execution. The results of th...
Article
Full-text available
One of the defining characteristics of human cognition is our outstanding capacity to cooperate. A central requirement for cooperation is the ability to establish a “shared plan”-which defines the interlaced actions of the two cooperating agents-in real time, and even to negotiate this shared plan during its execution. In the current research we id...
Article
Full-text available
A short G1 phase is a characteristic feature of mouse embryonic stem cells (ESCs). To determine if there is a causal relationship between G1 phase restriction and pluripotency, we made use of the Fluorescence Ubiquitination Cell Cycle Indicator (FUCCI) reporter system to FACS-sort ESCs in the different cell cycle phases. Hence, the G1 phase cells a...
Conference Paper
Full-text available
The goal of this research is to provide a real-time and adaptive spoken langue interface between humans and a humanoid robot. The system should be able to learn new grammatical constructions in real-time, and then use them immediately following or in a later interactive session. In order to achieve this we use a recurrent neural network of 500 neur...

Projects

Project (1)
Archived project
The What You Say Is What You Did project (WYSIWYD) will create a new transparency in human robot interaction (HRI) by allowing robots to both understand their own actions and those of humans, and to interpret and communicate these in human compatible intentional terms expressed as a language-like communication channel we call WYSIWYD Robotese (WR). WYSIWYD will advance this critical communication channel following a biologically and psychologically grounded developmental perspective allowing the robot to acquire, retain and express WR dependent on its individual interaction history. WYSIWYD will contribute to a qualitative change in human-robot interaction (HRI) and cooperation, unlocking new capabilities and application areas together with enhanced safety, robustness and monitoring. http://wysiwyd.upf.edu