Author name cluster

Thibaut Munzer

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

ICRA Conference 2017 Conference Paper

Preference learning on the execution of collaborative human-robot tasks

Thibaut Munzer
Marc Toussaint
Manuel Lopes 0001

We present a novel method to learn human preferences during, and for, the execution of concurrent joint humanrobot tasks. We consider tasks realized by a team of a human operator and a robot helper that should adapt to the human's task execution preferences. Different human operators can have different abilities, experiences, and personal preferences, so that a particular allocation of activities in the team is preferred over another. We cast the behavior of concurrent multi-agent cooperation as a semi Markov Decision Process and show how to model and learn human preferences over the team behavior. After proposing two different interactive learning algorithms, we evaluate them and show that the system can effectively learn and adapt to human preferences.

Details

ICRA Conference 2016 Conference Paper

Relational activity processes for modeling concurrent cooperation

Marc Toussaint
Thibaut Munzer
Yoan Mollard
Li Yang Wu
Ngo Anh Vien
Manuel Lopes 0001

In human-robot collaboration, multi-agent domains, or single-robot manipulation with multiple end-effectors, the activities of the involved parties are naturally concurrent. Such domains are also naturally relational as they involve objects, multiple agents, and models should generalize over objects and agents. We propose a novel formalization of relational concurrent activity processes that allows us to transfer methods from standard relational MDPs, such as Monte-Carlo planning and learning from demonstration, to concurrent cooperation domains. We formally compare the formulation to previous propositional models of concurrent decision making and demonstrate planning and learning from demonstration methods on a real-world human-robot assembly task.

Details

IJCAI Conference 2015 Conference Paper

Inverse Reinforcement Learning in Relational Domains

Thibaut Munzer
Bilal Piot
Matthieu Geist
Olivier Pietquin
Manuel Lopes

In this work, we introduce the first approach to the Inverse Reinforcement Learning (IRL) problem in relational domains. IRL has been used to recover a more compact representation of the expert policy leading to better generalization performances among different contexts. On the other hand, relational learning allows representing problems with a varying number of objects (potentially infinite), thus provides more generalizable representations of problems and skills. We show how these different formalisms allow one to create a new IRL algorithm for relational domains that can recover with great efficiency rewards from expert data that have strong generalization and transfer properties. We evaluate our algorithm in representative tasks and study the impact of diverse experimental conditions such as: the number of demonstrations, knowledge about the dynamics, transfer among varying dimensions of a problem, and changing dynamics.

PDF Details

IROS Conference 2015 Conference Paper

Robot programming from demonstration, feedback and transfer

Yoan Mollard
Thibaut Munzer
Andrea Baisero
Marc Toussaint
Manuel Lopes 0001

This paper presents a novel approach for robot instruction for assembly tasks. We consider that robot programming can be made more efficient, precise and intuitive if we leverage the advantages of complementary approaches such as learning from demonstration, learning from feedback and knowledge transfer. Starting from low-level demonstrations of assembly tasks, the system is able to extract a high-level relational plan of the task. A graphical user interface (GUI) allows then the user to iteratively correct the acquired knowledge by refining high-level plans, and low-level geometrical knowledge of the task. This combination leads to a faster programming phase, more precise than just demonstrations, and more intuitive than just through a GUI. A final process allows to reuse high-level task knowledge for similar tasks in a transfer learning fashion. Finally we present a user study illustrating the advantages of this approach.

Details