Gregory Dudek

The association of mobile devices with network resources (e.g., base stations, frequency bands/channels), known as load balancing, is critic… (voir plus)al to reduce communication traffic congestion and network performance. Reinforcement learning (RL) has shown to be effective for communication load balancing and achieves better performance than currently used rule-based methods, especially when the traffic load changes quickly. However, RL-based methods usually need to interact with the environment for a large number of time steps to learn an effective policy and can be difficult to tune. In this work, we aim to improve the data efficiency of RL-based solutions to make them more suitable and applicable for real-world applications. Specifically, we propose a simple, yet efficient and effective deep RL-based wireless network load balancing framework. In this solution, a set of good initialization values for control actions are selected with some cost-efficient approach to center the training of the RL agent. Then, a deep RL-based agent is trained to find offsets from the initialization values that optimize the load balancing problem. Experimental evaluation on a set of dynamic traffic scenarios demonstrates the effectiveness and efficiency of the proposed method.

2023-12-04

GLOBECOM 2023 - 2023 IEEE Global Communications Conference (publié)

Working Backwards: Learning to Place by Picking

Oliver Limoyo

Trevor Ablett

Jonathan Kelly

Francois Hogan

We present placing via picking (PvP), a method to autonomously collect real-world demonstrations for a family of placing tasks in which obje… (voir plus)cts must be manipulated to specific, contact-constrained locations. With PvP, we approach the collection of robotic object placement demonstrations by reversing the grasping process and exploiting the inherent symmetry of the pick and place problems. Specifically, we obtain placing demonstrations from a set of grasp sequences of objects initially located at their target placement locations. Our system can collect hundreds of demonstrations in contact-constrained environments without human intervention using two modules: compliant control for grasping and tactile regrasping. We train a policy directly from visual observations through behavioural cloning, using the autonomously-collected demonstrations. By doing so, the policy can generalize to object placement scenarios outside of the training environment without privileged information (e.g., placing a plate picked up from a table). We validate our approach in home robot scenarios that include dishwasher loading and table setting. Our approach yields robotic placing policies that outperform policies trained with kinesthetic teaching, both in terms of success rate and data efficiency, while requiring no human supervision.

2023-12-04

ArXiv (prépublication)

Working Backwards: Learning to Place by Picking

Oliver Limoyo

Trevor Ablett

Jonathan Kelly

Francois Hogan

We present placing via picking (PvP), a method to autonomously collect real-world demonstrations for a family of placing tasks in which obje… (voir plus)cts must be manipulated to specific, contact-constrained locations. With PvP, we approach the collection of robotic object placement demonstrations by reversing the grasping process and exploiting the inherent symmetry of the pick and place problems. Specifically, we obtain placing demonstrations from a set of grasp sequences of objects initially located at their target placement locations. Our system can collect hundreds of demonstrations in contact-constrained environments without human intervention using two modules: compliant control for grasping and tactile regrasping. We train a policy directly from visual observations through behavioural cloning, using the autonomously-collected demonstrations. By doing so, the policy can generalize to object placement scenarios outside of the training environment without privileged information (e.g., placing a plate picked up from a table). We validate our approach in home robot scenarios that include dishwasher loading and table setting. Our approach yields robotic placing policies that outperform policies trained with kinesthetic teaching, both in terms of success rate and data efficiency, while requiring no human supervision.

2023-12-04

ArXiv (prépublication)

A Study of Human-Robot Handover through Human-Human Object Transfer

Charlotte Morissette

Bobak H. Baghi

Francois Hogan

In this preliminary study, we investigate changes in handover behaviour when transferring hazardous objects with the help of a high-resoluti… (voir plus)on touch sensor. Participants were asked to hand over a safe and hazardous object (a full cup and an empty cup) while instrumented with a modified STS sensor. Our data shows a clear distinction in the length of handover for the full cup vs the empty one, with the former being slower. Sensor data further suggests a change in tactile behaviour dependent on the object's risk factor. The results of this paper motivate a deeper study of tactile factors which could characterize a risky handover, allowing for safer human-robot interactions in the future.

2023-11-21

ArXiv (prépublication)

SAGE: Smart home Agent with Grounded Execution

Dmitriy Rivkin

Francois Hogan

Amal Feriani

Adam Sigal

Steve Liu

2023-11-01

ArXiv (prépublication)

Realizing XR Applications Using 5G-Based 3D Holographic Communication and Mobile Edge Computing

Ekram Hossain

3D holographic communication has the potential to revolutionize the way people interact with each other in virtual spaces, offering immersiv… (voir plus)e and realistic experiences. However, demands for high data rates, extremely low latency, and high computations to enable this technology pose a significant challenge. To address this challenge, we propose a novel job scheduling algorithm that leverages Mobile Edge Computing (MEC) servers in order to minimize the total latency in 3D holographic communication. One of the motivations for this work is to prevent the uncanny valley effect, which can occur when the latency hinders the seamless and real-time rendering of holographic content, leading to a less convincing and less engaging user experience. Our proposed algorithm dynamically allocates computation tasks to MEC servers, considering the network conditions, computational capabilities of the servers, and the requirements of the 3D holographic communication application. We conduct extensive experiments to evaluate the performance of our algorithm in terms of latency reduction, and the results demonstrate that our approach significantly outperforms other baseline methods. Furthermore, we present a practical scenario involving Augmented Reality (AR), which not only illustrates the applicability of our algorithm but also highlights the importance of minimizing latency in achieving high-quality holographic views. By efficiently distributing the computation workload among MEC servers and reducing the overall latency, our proposed algorithm enhances the user experience in 3D holographic communications and paves the way for the widespread adoption of this technology in various applications, such as telemedicine, remote collaboration, and entertainment.

2023-10-06

ArXiv (prépublication)

A Generic Framework for Byzantine-Tolerant Consensus Achievement in Robot Swarms

Hanqing Zhao

Alexandre Pacheco

Volker Strobel

Andreagiovanni Reina

Marco Dorigo

Recent studies show that some security features that blockchains grant to decentralized networks on the internet can be ported to swarm robo… (voir plus)tics. Although the integration of blockchain technology and swarm robotics shows great promise, thus far, research has been limited to proof-of-concept scenarios where the blockchain-based mechanisms are tailored to a particular swarm task and operating environment. In this study, we propose a generic framework based on a blockchain smart contract that enables robot swarms to achieve secure consensus in an arbitrary observation space. This means that our framework can be customized to fit different swarm robotics missions, while providing methods to identify and neutralize Byzantine robots, that is, robots which exhibit detrimental behaviours stemming from faults or malicious tampering.

2023-10-01

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publié)

Zero-Shot Fault Detection for Manipulators Through Bayesian Inverse Reinforcement Learning

Hanqing Zhao

We consider the detection of faults in robotic manipulators, with particular emphasis on faults that have not been observed or identified in… (voir plus) advance, which naturally includes those that occur very infrequently. Recent studies indicate that the reward function obtained through Inverse Reinforcement Learning (IRL) can help detect anomalies caused by faults in a control system (i.e. fault detection). Current IRL methods for fault detection, however, either use a linear reward representation or require extensive sampling from the environment to estimate the policy, rendering them inappropriate for safety-critical situations where sampling of failure observations via fault injection can be expensive and dangerous. To address this issue, this paper proposes a zero-shot and exogenous fault detector based on an approximate variational reward imitation learning (AVRIL) structure. The fault detector recovers a reward signal as a function of externally observable information to describe the normal operation, which can then be used to detect anomalies caused by faults. Our method incorporates expert knowledge through a customizable reward prior distribution, allowing the fault detector to learn the reward solely from normal operation samples, without the need for a simulator or costly interactions with the environment. We evaluate our approach for exogenous partial fault detection in multi-stage robotic manipulator tasks, comparing it with several baseline methods. The results demonstrate that our method more effectively identifies unseen faults even when they occur within just three controller time steps.

2023-10-01

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publié)

Hypernetworks for Zero-shot Transfer in Reinforcement Learning

Sahand Rezaei-Shoshtari

Charlotte Morissette

Francois Hogan

David Meger

In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objec… (voir plus)tive and data from a set of near-optimal RL solutions for training tasks. This work relates to meta RL, contextual RL, and transfer learning, with a particular focus on zero-shot performance at test time, enabled by knowledge of the task parameters (also known as context). Our technical approach is based upon viewing each RL algorithm as a mapping from the MDP specifics to the near-optimal value function and policy and seek to approximate it with a hypernetwork that can generate near-optimal value functions and policies, given the parameters of the MDP. We show that, under certain conditions, this mapping can be considered as a supervised learning problem. We empirically evaluate the effectiveness of our method for zero-shot transfer to new reward and transition dynamics on a series of continuous control tasks from DeepMind Control Suite. Our method demonstrates significant improvements over baselines from multitask and meta RL approaches.

2023-06-26

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

CeBed: A Benchmark for Deep Data-Driven OFDM Channel Estimation

Amal Feriani

Di Wu

Steve Liu

2023-06-23

ArXiv (prépublication)

ANSEL Photobot: A Robot Event Photographer with Semantic Intelligence

Dmitriy Rivkin

Nikhil Kakodkar

David Meger

Oliver Limoyo

Francois Hogan

Our work examines the way in which large language models can be used for robotic planning and sampling in the context of automated photograp… (voir plus)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) (publié)

Communication Load Balancing via Efficient Inverse Reinforcement Learning

Di Wu

Yi Tian Xu

Seowoo Jang

Steve Liu

Communication load balancing aims to balance the load between different available resources, and thus improve the quality of service for net… (voir plus)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 (publié)