15 accepted papers
Guidance of generative models is typically achieved by modifying the probability flow vector field through the addition of a guidance field. In this paper, we instead propose the Source-Guided Flow Matching (SGFM) framework, which modifies the source distribution directly while keeping the pre-train…
In Amazon robotic warehouses, the destination-to-chute mapping problem is crucial for efficient package sorting. Often, however, this problem is complicated by uncertain and dynamic package induction rates, which can lead to increased package recirculation. To tackle this challenge, we introduce a D…
Learning to cooperate in distributed partially observable environments with no communication abilities poses significant challenges for multi-agent deep reinforcement learning (MARL). This paper addresses key concerns in this domain, focusing on inferring state representations from individual agent…
Distributionally Robust Optimization (DRO) accounts for uncertainty in data distributions by optimizing the model performance against the worst possible distribution within an ambiguity set. In this paper, we propose a DRO framework that relies on a new distance inspired by Unbalanced Optimal Transp…
Training robots with reinforcement learning (RL) typically involves heavy interactions with the environment, and the acquired skills are often sensitive to changes in task environments and robot kinematics. Transfer RL aims to leverage previous knowledge to accelerate learning of new tasks or new bo…
In this work, we address the problem of formal safety verification for stochastic cyber-physical systems (CPS) equipped with ReLU neural network (NN) controllers. Our goal is to find the set of initial states from where, with a predetermined confidence, the system will not reach an unsafe configurat…
Planning the motion of bearings-only sensors is critical for enabling accurate tracking of the positions of moving targets. In this paper, we demonstrate planning the observer's motion over horizons greater than one step for estimating an unknown and varying number of indistinguishable, maneuvering…
In this letter we propose an algorithm to actively track multiple moving targets using a bearing-only sensor in the presence of merged measurements. Merged measurements arise from sensor resolution constraints and therefore targets that are close in relative bearing to the sensor get reported as a s
In this letter, we propose an algorithm to estimate the distance between an aerial vehicle and a large obstacle using the self-induced noise present during the vehicle's normal operation. We demonstrate the feasibility of using the proposed estimation method in real-time as a feedback mechanism to a
We study synthesis of control strategies from linear temporal logic (LTL) objectives in unknown environments. We model this problem as a turn-based zero-sum stochastic game between the controller and the environment, where the transition probabilities and the model topology are fully unknown. The wi…
In this paper we propose an algorithm to actively control the distance of a motor-propeller system (MPS) to a large obstacle using data from a single microphone. The method is based upon a broadband constructive/destructive interference pattern across the audible frequency band that is present when…
We present a reinforcement learning (RL) frame-work to synthesize a control policy from a given linear temporal logic (LTL) specification in an unknown stochastic environment that can be modeled as a Markov Decision Process (MDP). Specifically, we learn a policy that maximizes the probability of sat…
In this paper, we propose a Deep Imitative Q-learning (DIQL) method to synthesize control policies for mobile robots that need to satisfy Linear Temporal Logic (LTL) specifications using noisy semantic observations of their surroundings. The robot sensing error is modeled using probabilistic labels…
In this paper, we develop a distributed intermittent communication framework for teams of mobile robots that are responsible for accomplishing time-critical dynamic tasks and sharing the collected information with all other robots and possibly also with a user. Specifically, we consider situations w…
We consider a team of multiple dynamical and heterogeneous robots which are deployed for gathering different types of data within a common workspace. The robots have different roles due to different capabilities: some gather data from the workspace (Type-A robots) and others receive data from Type-A…