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Developing deep learning models that effectively learn object-centric representations, akin to human cognition, remains a challenging task. … (see more)Existing approaches facilitate object discovery by representing objects as fixed-size vectors, called ``slots'' or ``object files''. While these approaches have shown promise in certain scenarios, they still exhibit certain limitations. First, they rely on architectural priors which can be unreliable and usually require meticulous engineering to identify the correct objects. Second, there has been a notable gap in investigating the practical utility of these representations in downstream tasks. To address the first limitation, we introduce a method that explicitly optimizes the constraint that each object in a scene should be associated with a distinct slot. We formalize this constraint by introducing consistency objectives which are cyclic in nature. By integrating these consistency objectives into various existing slot-based object-centric methods, we showcase substantial improvements in object-discovery performance. These enhancements consistently hold true across both synthetic and real-world scenes, underscoring the effectiveness and adaptability of the proposed approach. To tackle the second limitation, we apply the learned object-centric representations from the proposed method to two downstream reinforcement learning tasks, demonstrating considerable performance enhancements compared to conventional slot-based and monolithic representation learning methods. Our results suggest that the proposed approach not only improves object discovery, but also provides richer features for downstream tasks.
Our work examines the way in which large language models can be used for robotic planning and sampling in the context of automated photograp… (see more)hic documentation. Specifically, we illustrate how to produce a photo-taking robot with an exceptional level of semantic awareness by leveraging recent advances in general purpose language (LM) and vision-language (VLM) models. Given a high-level description of an event we use an LM to generate a natural-language list of photo descriptions that one would expect a photographer to capture at the event. We then use a VLM to identify the best matches to these descriptions in the robot's video stream. The photo portfolios generated by our method are consistently rated as more appropriate to the event by human evaluators than those generated by existing methods.
2023-06-02
2023 IEEE International Conference on Robotics and Automation (ICRA) (published)
The need for rapid and reliable robot deployment is on the rise. Imitation Learning (IL) has become popular for producing motion planning po… (see more)licies from a set of demonstrations. However, many methods in IL are not guaranteed to produce stable policies. The generated policy may not converge to the robot target, reducing reliability, and may collide with its environment, reducing the safety of the system. Stable Estimator of Dynamic Systems (SEDS) produces stable policies by constraining the Lyapunov stability criteria during learning, but the Lyapunov candidate function had to be manually selected. In this work, we propose a novel method for learning a Lyapunov function and a collision-free policy using a single neural network model. The method can be equipped with an obstacle avoidance module for convex object pairs to guarantee no collisions. We demonstrated our method is capable of finding policies in several simulation environments and transfer to a real-world scenario.
2023-06-02
2023 IEEE International Conference on Robotics and Automation (ICRA) (published)
Predicting Time to and Average Quality of Future Offers for Kidney Transplant Candidates Declining a Current Deceased Donor Kidney Offer: A Retrospective Cohort Study
Jonathan Jalbert
Jean-Noel Weller
Pierre-Luc Boivin
Sylvain Lavigne
Mehdi Taobane
Mike Pieper
Andrea Lodi
Heloise Cardinal
2023-06-02
Canadian Journal of Kidney Health and Disease (published)
Communication load balancing aims to balance the load between different available resources, and thus improve the quality of service for net… (see more)work systems. After formulating the load balancing (LB) as a Markov decision process problem, reinforcement learning (RL) has recently proven effective in addressing the LB problem. To leverage the benefits of classical RL for load balancing, however, we need an explicit reward definition. Engineering this reward function is challenging, because it involves the need for expert knowledge and there lacks a general consensus on the form of an optimal reward function. In this work, we tackle the communication load balancing problem from an inverse reinforcement learning (IRL) approach. To the best of our knowledge, this is the first time IRL has been successfully applied in the field of communication load balancing. Specifically, first, we infer a reward function from a set of demonstrations, and then learn a reinforcement learning load balancing policy with the inferred reward function. Compared to classical RL-based solution, the proposed solution can be more general and more suitable for real-world scenarios. Experimental evaluations implemented on different simulated traffic scenarios have shown our method to be effective and better than other baselines by a considerable margin.
2023-06-01
ICC 2023 - IEEE International Conference on Communications (published)
Estimating individual minimum calibration for deep-learning with predictive performance recovery: An example case of gait surface classification from wearable sensor gait data.
Current deep neural networks (DNNs) have achieved remarkable accuracy in various downstream tasks. However, their training and fine-tuning a… (see more)re challenging due to several factors, such as limited computational resources, extended training and fine-tuning times, and over-fitting due to small datasets. To address these challenges, we propose a three-stage fast fine-tuning method that efficiently trains DNNs for edge devices. Our method combines curriculum learning and domain adaptation techniques to accelerate training while achieving comparable performance. First, we develop a data curriculum approach, which ranks the dataset according to difficulty and split it into the source domain (containing easy data) and the target domain (containing difficult data). Second, we adapt the pretrained model from the source domain to the target domain using an unsupervised domain adaptation (UDA) method called Deep CORAL. Finally, we continue training the adapted model on the source domain with fewer epochs. Our method achieves high accuracy quickly on various modern neural network architectures and datasets such as CIFAR-10, CIFAR-100, and CINIC-10.
2023-06-01
Canadian Conference on Computer and Robot Vision (published)
A fundamental task in data exploration is to extract low dimensional representations that capture intrinsic geometry in data, especially for… (see more) faithfully visualizing data in two or three dimensions. Common approaches use kernel methods for manifold learning. However, these methods typically only provide an embedding of the input data and cannot extend naturally to new data points. Autoencoders have also become popular for representation learning. While they naturally compute feature extractors that are extendable to new data and invertible (i.e., reconstructing original features from latent representation), they often fail at representing the intrinsic data geometry compared to kernel-based manifold learning. We present a new method for integrating both approaches by incorporating a geometric regularization term in the bottleneck of the autoencoder. This regularization encourages the learned latent representation to follow the intrinsic data geometry, similar to manifold learning algorithms, while still enabling faithful extension to new data and preserving invertibility. We compare our approach to autoencoder models for manifold learning to provide qualitative and quantitative evidence of our advantages in preserving intrinsic structure, out of sample extension, and reconstruction. Our method is easily implemented for big-data applications, whereas other methods are limited in this regard.
2023-06-01
IEEE Transactions on Pattern Analysis and Machine Intelligence (published)
