Julien Perez

Attention-based cloth manipulation from model-free topological representation

By Kevin Galassi, Bingbing Wu, Julien Perez, Gianluca Palli, Jean-Michel Renders

2024-10-10

In IEEE international conference on robotics and automation, ICRA 2024, yokohama, japan, may 13-17, 2024

Abstract

The robotic manipulation of deformable objects, such as clothes and fabric, is known as a complex task from both the perception and planning perspectives. Indeed, the stochastic nature of the underlying environment dynamics makes it an interesting research field for statistical learning approaches and neural policies. In this work, we introduce a novel attention- based neural architecture capable of solving a smoothing task for such objects by means of a single robotic arm. To train our network, we leverage an oracle policy, executed in simulation, which uses the topological description of a mesh of points for representing the object to smooth. In a second step, we transfer the resulting behavior in the real world with imitation learning using the cloth point cloud as decision support, which is captured from a single RGBD camera placed egocentrically on the wrist of the arm. This approach allows fast training of the real-world manipulation neural policy while not requiring scene reconstruction at test time, but solely a point cloud acquired from a single RGBD camera. Our resulting policy first predicts the desired point to choose from the given point cloud and then the correct displacement to achieve a smoothed cloth. Experimentally, we first assess our results in a simulation environment by comparing them with an existing heuristic policy, as well as several baseline attention architectures. Then, we validate the performance of our approach in a real-world scenario

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Unsupervised skill discovery for robotic manipulation through automatic task generation

By David Emukpere, Bingbing Wu, Julien Perez, Jean-Michel Renders

2024-10-10

In IEEE international conference on robotics and automation, ICRA 2024, yokohama, japan, may 13-17, 2024

Abstract

Self-supervised skill learning aims to acquire useful behaviors that leverage the underlying dynamics of the environment. Latent variable models, based on mutual information maximization, have been successful in this task but still struggle in the context of robotic manipulation. As it requires impacting a possibly large set of degrees of freedom composing the environment, mutual information maximization fails alone in producing useful and safe manipulation behaviors. Furthermore, tackling this by augmenting skill discovery rewards with additional rewards through a naive combination might fail to produce desired behaviors. To address this limitation, we introduce SLIM, a multi-critic learning approach for skill discovery with a particular focus on robotic manipulation. Our main insight is that utilizing multiple critics in an actor-critic framework to gracefully combine multiple reward functions leads to a significant improvement in latent-variable skill discovery for robotic manipulation while overcoming possible interference occurring among rewards which hinders convergence to useful skills. Furthermore, in the context of tabletop manipulation, we demonstrate the applicability of our novel skill discovery approach to acquire safe and efficient motor primitives in a hierarchical reinforcement learning fashion and leverage them through planning, significantly surpassing baseline approaches for skill discovery.

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Unsupervised skill discovery for robotic manipulation through automatic task generation

By Paul Jansonnie, Bingbing Wu, Julien Perez, Jan Peters

2024-10-10

In IEEE-RAS international conference on humanoid robots

Abstract

Learning skills that interact with objects is of major importance for robotic manipulation. These skills can indeed serve as an efficient prior for solving various manipulation tasks. We propose a novel Skill Learning approach that discovers composable behaviors by solving a large and diverse number of autonomously generated tasks. Our method learns skills allowing the robot to consistently and robustly interact with objects in its environment. The discovered behaviors are embedded in primitives which can be composed with Hierarchical Reinforcement Learning to solve unseen manipulation tasks. In particular, we leverage Asymmetric Self-Play to discover behaviors and Multiplicative Compositional Policies to embed them. We compare our method to Skill Learning baselines and find that our skills are more interactive. Furthermore, the learned skills can be used to solve a set of unseen manipulation tasks, in simulation as well as on a real robotic platform.

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DiffVersify: A scalable approach to differentiable pattern mining with coverage regularization

By Thibaut Chataing, Julien Perez, Marc Plantevit, Céline Robardet

2024-01-10

In Machine learning and knowledge discovery in databases. Research track - european conference, ECML PKDD 2024, vilnius, lithuania, september 9-13, 2024, proceedings, part VI

Abstract

Pattern mining addresses the challenge of automatically identifying interpretable and discriminative patterns within data. Recent approaches, leveraging differentiable approach through neural autoencoder with class recovery, have achieved encouraging results but tend to fall short as the magnitude of the noise and the number of underlying features increase in the data. Empirically, one can observe that the number of discovered patterns tend to be limited in these challenging contexts. In this article, we present a differentiable binary model that integrates a new regularization technique to enhance pattern coverage. Besides, we introduce an innovative pattern decoding strategy taking advantage of non-negative matrix factorization (NMF), extending beyond conventional thresholding methods prevalent in existing approaches. Experiments on four realworld datasets exhibit superior performances of DiffVersify in terms of the ROC-AUC metric. On synthetic data, we observe an increase in the similarity between the discovered patterns and the ground truth. Finally, using several metrics to finely evaluate the quality of the patterns in regard to the data, we show the global effectiveness of the approach.

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