Abhisek Konar

Adam Sigal

Xue (Steve) Liu

2025-02-01

IEEE Internet of Things Journal (publié)

AIoT Smart Home via Autonomous LLM Agents

Dmitriy Rivkin

Francois Hogan

Amal Feriani

Adam Sigal

Xue (Steve) Liu

The common-sense reasoning abilities and vast general knowledge of large language models (LLMs) make them a natural fit for interpreting use… (voir plus)r requests in a smart home assistant context. LLMs, however, lack specific knowledge about the user and their home, which limits their potential impact. Smart home agent with grounded execution (SAGE), overcomes these and other limitations by using a scheme in which a user request triggers an LLM-controlled sequence of discrete actions. These actions can be used to retrieve information, interact with the user, or manipulate device states. SAGE controls this process through a dynamically constructed tree of LLM prompts, which help it decide which action to take next, whether an action was successful, and when to terminate the process. The SAGE action set augments an LLM’s capabilities to support some of the most critical requirements for a smart home assistant. These include: flexible and scalable user preference management (“Is my team playing tonight?”), access to any smart device’s full functionality without device-specific code via API reading (“Turn down the screen brightness on my dryer”), persistent device state monitoring (“Remind me to throw out the milk when I open the fridge”), natural device references using only a photo of the room (“Turn on the lamp on the dresser”), and more. We introduce a benchmark of 50 new and challenging smart home tasks where SAGE achieves a 76% success rate, significantly outperforming existing LLM-enabled baselines (30% success rate).

2025-02-01

IEEE Internet of Things Journal (publié)

Working Backwards: Learning to Place by Picking

Oliver Limoyo

Trevor Ablett

Jonathan Kelly

Francois Hogan

2024-10-14

2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publié)

Accelerating Digital Twin Calibration with Warm-Start Bayesian Optimization

Amal Feriani

Seowoo Jang

Digital twins are expected to play an important role in the widespread adaptation of AI-based networking solutions in the real world. The ca… (voir plus)libration of these virtual replicas is critical to ensure a trustworthy replication of the real environment. This work focuses on the input parameter calibration of radio access network (RAN) simulators using real network performance metrics as supervision signals. Usually, the RAN digital twin is considered a black-box function and each calibration problem is viewed as a standalone search problem. RAN simulators are slow and non-differentiable, often posing as the bottleneck in the execution time for these search problems. In this work, we aim to accelerate the search process by reducing the number of interactions with the simulator by leveraging RAN interactions from previous problems. We present a sequential Bayesian optimization framework that uses information from the past to warm-start the calibration process. Assuming that the network performance exhibits gradual and periodic changes, the stored information can be reused in future calibrations. We test our method across multiple physical sites over one week and show that using the proposed framework, we can obtain better calibration with a smaller number of interactions with the simulator during the search phase.

2024-06-09

ICC 2024 - IEEE International Conference on Communications (publié)

AIoT Smart Home via Autonomous LLM Agents

Dmitriy Rivkin

Francois Hogan

Amal Feriani

Adam Sigal

Xue (Steve) Liu

2024-01-01

IEEE Internet of Things Journal (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)

SAGE: Smart home Agent with Grounded Execution

Dmitriy Rivkin

Francois Hogan

Amal Feriani

Adam Sigal

Steve Liu

2023-11-01

ArXiv (prépublication)

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)

Mixed-Variable PSO with Fairness on Multi-Objective Field Data Replication in Wireless Networks

Dun Yuan

Yujin Nam

Amal Feriani

Seowoo Jang

Digital twins have shown a great potential in supporting the development of wireless networks. They are virtual representations of 5G/6G sys… (voir plus)tems enabling the design of machine learning and optimization-based techniques. Field data replication is one of the critical aspects of building a simulation-based twin, where the objective is to calibrate the simulation to match field performance measurements. Since wireless networks involve a variety of key performance indicators (KPIs), the replication process becomes a multi-objective optimization problem in which the purpose is to minimize the error between the simulated and field data KPIs. Unlike previous works, we focus on designing a data-driven search method to calibrate the simulator and achieve accurate and reliable reproduction of field performance. This work proposes a search-based algorithm based on mixed-variable particle swarm optimization (PSO) to find the optimal simulation parameters. Furthermore, we extend this solution to account for potential conflicts between the KPIs using a-fairness concept to adjust the importance attributed to each KPI during the search. Experiments on field data showcase the effectiveness of our approach to (i) improve the accuracy of the replication, (ii) enhance the fairness between the different KPIs, and (iii) guarantee faster convergence compared to other methods.

2023-06-01

ICC 2023 - IEEE International Conference on Communications (publié)