More Like this

1. Joint Communication and Motion Planning for Cobots

   https://doi.org/10.1109/ICRA46639.2022.9812261  

   Dadvar, Mehdi; Majd, Keyvan; Oikonomou, Elena; Fainekos, Georgios; Srivastava, Siddharth (May 2022, IEEE International Conference on Robotics and Automation)

   The increasing deployment of robots in co-working scenarios with humans has revealed complex safety and efficiency challenges in the computation of the robot behavior. Movement among humans is one of the most fundamental —and yet critical—problems in this frontier. While several approaches have addressed this problem from a purely navigational point of view, the absence of a unified paradigm for communicating with humans limits their ability to prevent deadlocks and compute feasible solutions. This paper presents a joint communication and motion planning framework that selects from an arbitrary input set of robot's communication signals while computing robot motion plans. It models a human co-worker's imperfect perception of these communications using a noisy sensor model and facilitates the specification of a variety of social/workplace compliance priorities with a flexible cost function. Theoretical results and simulator-based empirical evaluations show that our approach efficiently computes motion plans and communication strategies that reduce conflicts between agents and resolve potential deadlocks. 

   more » « less
2. Enforcing Signal Temporal Logic Specifications in Multi-Agent Adversarial Environments: A Deep Q-Learning Approach

   https://doi.org/10.1109/CDC.2018.8618746  

   Muniraj, D; Vamvoudakis, K. G; Farhood, M. (January 2019, 2018 IEEE Conference on Decision and Control (CDC))

   This work addresses the problem of learning optimal control policies for a multi-agent system in an adversarial environment. Specifically, we focus on multi-agent systems where the mission objectives are expressed as signal temporal logic (STL) specifications. The agents are classified as either defensive or adversarial. The defensive agents are maximizers, namely, they maximize an objective function that enforces the STL specification; the adversarial agents, on the other hand, are minimizers. The interaction among the agents is modeled as a finite-state team stochastic game with an unknown transition probability function. The synthesis objective is to determine optimal control policies for the defensive agents that implement the STL specification against the best responses of the adversarial agents. A multi-agent deep Q-learning algorithm, which is an extension of the minimax Q-learning algorithm, is then proposed to learn the optimal policies. The effectiveness of the proposed approach is illustrated through a simulation case study. 

   more » « less
3. Performance studies of evolutionary transfer learning for end-to-end QoT estimation in multi-domain optical networks [Invited]

   https://doi.org/10.1364/JOCN.409817  

   Liu, Che-Yu; Chen, Xiaoliang; Proietti, Roberto; Yoo, S_J_Ben (January 2021, Journal of Optical Communications and Networking)

   This paper proposes an evolutionary transfer learning approach (Evol-TL) for scalable quality-of-transmission (QoT) estimation in multi-domain elastic optical networks (MD-EONs). Evol-TL exploits a broker-based MD-EON architecture that enables cooperative learning between the broker plane (end-to-end) and domain-level (local) machine learning functions while securing the autonomy of each domain. We designed a genetic algorithm to optimize the neural network architectures and the sets of weights to be transferred between the source and destination tasks. We evaluated the performance of Evol-TL with three case studies considering the QoT estimation task for lightpaths with (i) different path lengths (in terms of the numbers of fiber links traversed), (ii) different modulation formats, and (iii) different device conditions (emulated by introducing different levels of wavelength-specific attenuation to the amplifiers). The results show that the proposed approach can reduce the average amount of required training data by up to $13\times <\#comment/>$while achieving an estimation accuracy above 95%. 

   more » « less
4. Estimation of Multi-Attribute Differential Graphs with Non-Convex Penalties

   https://doi.org/10.1109/ICASSP49660.2025.10887671  

   Tugnait, Jitendra K (April 2025, IEEE)

   We consider the problem of estimating differences in two multi-attribute Gaussian graphical models (GGMs) which are known to have similar structure, using a penalized D-trace loss function with nonconvex penalties. The GGM structure is encoded in its precision (inverse covariance) matrix. Existing methods for multi-attribute differential graph estimation are based on a group lasso penalized loss function. In this paper, we consider a penalized D-trace loss function with nonconvex (log-sum and smoothly clipped absolute deviation (SCAD)) penalties. Two proximal gradient descent methods are presented to optimize the objective function. Theoretical analysis establishing local consistency in support recovery, local convexity and estimation in high-dimensional settings is provided. We illustrate our approach with a numerical example. 

   more » « less
5. Generating Executable Action Plans with Environmentally-Aware Language Models

   https://doi.org/10.1109/IROS55552.2023.10341989  

   Gramopadhye, Maitrey; Szafir, Daniel (October 2023, IEEE)

   Large Language Models (LLMs) trained using massive text datasets have recently shown promise in generating action plans for robotic agents from high level text queries. However, these models typically do not consider the robot’s environment, resulting in generated plans that may not actually be executable, due to ambiguities in the planned actions or environmental constraints. In this paper, we propose an approach to generate environmentally-aware action plans that agents are better able to execute. Our approach involves integrating environmental objects and object relations as additional inputs into LLM action plan generation to provide the system with an awareness of its surroundings, resulting in plans where each generated action is mapped to objects present in the scene. We also design a novel scoring function that, along with generating the action steps and associating them with objects, helps the system disambiguate among object instances and take into account their states. We evaluated our approach using the VirtualHome simulator and the ActivityPrograms knowledge base and found that action plans generated from our system had a 310% improvement in executability and a 147% improvement in correctness over prior work. The complete code and a demo of our method is publicly available at https://github.com/hri-ironlab/scene_aware_language_planner. 

   more » « less