25 research outputs found
Goal Set Inverse Optimal Control and Iterative Re-planning for Predicting Human Reaching Motions in Shared Workspaces
To enable safe and efficient human-robot collaboration in shared workspaces
it is important for the robot to predict how a human will move when performing
a task. While predicting human motion for tasks not known a priori is very
challenging, we argue that single-arm reaching motions for known tasks in
collaborative settings (which are especially relevant for manufacturing) are
indeed predictable. Two hypotheses underlie our approach for predicting such
motions: First, that the trajectory the human performs is optimal with respect
to an unknown cost function, and second, that human adaptation to their
partner's motion can be captured well through iterative re-planning with the
above cost function. The key to our approach is thus to learn a cost function
which "explains" the motion of the human. To do this, we gather example
trajectories from pairs of participants performing a collaborative assembly
task using motion capture. We then use Inverse Optimal Control to learn a cost
function from these trajectories. Finally, we predict reaching motions from the
human's current configuration to a task-space goal region by iteratively
re-planning a trajectory using the learned cost function. Our planning
algorithm is based on the trajectory optimizer STOMP, it plans for a 23 DoF
human kinematic model and accounts for the presence of a moving collaborator
and obstacles in the environment. Our results suggest that in most cases, our
method outperforms baseline methods when predicting motions. We also show that
our method outperforms baselines for predicting human motion when a human and a
robot share the workspace.Comment: 12 pages, Accepted for publication IEEE Transaction on Robotics 201
Prediction of Human Full-Body Movements with Motion Optimization and Recurrent Neural Networks
Human movement prediction is difficult as humans naturally exhibit complex
behaviors that can change drastically from one environment to the next. In
order to alleviate this issue, we propose a prediction framework that decouples
short-term prediction, linked to internal body dynamics, and long-term
prediction, linked to the environment and task constraints. In this work we
investigate encoding short-term dynamics in a recurrent neural network, while
we account for environmental constraints, such as obstacle avoidance, using
gradient-based trajectory optimization. Experiments on real motion data
demonstrate that our framework improves the prediction with respect to
state-of-the-art motion prediction methods, as it accounts to beforehand unseen
environmental structures. Moreover we demonstrate on an example, how this
framework can be used to plan robot trajectories that are optimized to
coordinate with a human partner.Comment: International Conference on Robotics and Automation (ICRA) 202
An Interior Point Method Solving Motion Planning Problems with Narrow Passages
Algorithmic solutions for the motion planning problem have been investigated
for five decades. Since the development of A* in 1969 many approaches have been
investigated, traditionally classified as either grid decomposition, potential
fields or sampling-based. In this work, we focus on using numerical
optimization, which is understudied for solving motion planning problems. This
lack of interest in the favor of sampling-based methods is largely due to the
non-convexity introduced by narrow passages. We address this shortcoming by
grounding the solution in differential geometry. We demonstrate through a
series of experiments on 3 Dofs and 6 Dofs narrow passage problems, how
modeling explicitly the underlying Riemannian manifold leads to an efficient
interior-point non-linear programming solution.Comment: IEEE RO-MAN 2020, 6 page
Planification de mouvement pour la manipulation d'objets sous contraintes d'interaction homme-robot
Un robot agit sur son environnement par le mouvement, sa capacité à planifier ses mouvements est donc une composante essentielle de son autonomie. L'objectif de cette thèse est concevoir des méthodes algorithmiques performantes permettant le calcul automatique de trajectoires pour des systèmes robotiques complexes dans le cadre de la robotique d'assistance. Les systèmes considérés qui ont pour vocation de servir l'homme et de l'accompagner dans des tâches du quotidien doivent tenir compte de la sécurité et du bien-être de l'homme. Pour cela, les mouvements du robot doivent être générés en considérant explicitement le partenaire humain raisonant sur un modèle du comportement social de l'homme, de ses capacités et de ses limites afin de produire un comportement synergique optimal.Dans cette thèse nous étendons les travaux pionniers menés au LAAS dans ce domaine afin de produire des mouvements considérant l homme de manière explicite dans des environnements encombrés. Des algorithmes d exploration de l espace des configurations par échantillonnage aléatoire sont combinés à des algorithmes d optimisation de trajectoire afin de produire des mouvements sûrs et agréables. Nous proposons dans un deuxième temps un planificateur de tâche d échange d objet prenant en compte la mobilité du receveur humain permettant ainsi de partager l effort lors du transfert. La pertinence de cette approche a été étudiée dans une étude utilisateur. Finalement, nous présentons une architecture logicielle qui permet de prendre en compte l homme de manière dynamique lors de la réalisation de tâches de manipulation interactiveA robot act upon its environment through motion, the ability to plan its movements is therefore an essential component of its autonomy. The objective of this thesis is to design algorithmic methods to perform automatic trajectory computation for complex robotic systems in the context of assistive robotics. This emerging field of autonomous robotics applications brings new constraints and new challenges. Such systems that are designed to serve humans and to help in daily tasks must consider the safety and well-being of the surrounding humans. To do this, the robot's motions must be generated by considering the human partner explicitly. For comfort and efficiency, the robot must take into account a model of human social behavior, capabilities and limitations to produce an optimal synergistic behavior.In this thesis we extend to cluttered environments the pioneering work that has been conducted at LAAS in this field. Algorithms that explore the configuration space by random sampling are combined with trajectory optimization algorithms to produce safe and human aware motions. Secondly we propose a planner for object handover taking into account the mobility of the human recipient allowing to share the effort during the transfer. The relevance of this approach has been studied in a user study. Finally, we present a software architecture developed in collaboration with a partner of the European project Dexmart that allows to take dynamically into account humans during the execution of interactive manipulation tasksTOULOUSE-INSA-Bib. electronique (315559905) / SudocSudocFranceF