arXiv subjects

Andrew Silva

Publications and source records attributed to Andrew Silva.

At least 19 recordsLinked to original sources

arXiv2026

Introduction to Quantum Ophthalmology

Quantum technologies are rapidly advancing across multiple research domains, with a growing impact on biomedical imaging and sensing. We examine their emerging role in ophthalmology through four complementary directions: photon-limited retinal imaging, correlation based imaging, nanoscale optical probes, and quantum-limited visual perception. Advances in optical coherence tomography and single-photon detection enable imaging under strict photon budget constraints, reducing phototoxicity while preserving image quality. Correlation-based approaches, including ghost imaging, offer alternative strategies for image formation in low-light and scattering environments, although practical implementation remains limited by detection efficiency and acquisition time. In parallel, nanoscale optical platforms such as quantum dots provide tunable and photostable probes for enhanced contrast and targeted delivery, with ongoing challenges related to biocompatibility and clinical translation. Finally, experiments at the single-photon level and with structured light fields demonstrate how the visual system itself operates near physical detection limits and can be probed using controlled optical states. While many of these approaches remain at an early stage, they collectively illustrate how quantum and quantum-inspired methods may augment current ophthalmic imaging and diagnostic technologies while providing new tools for studying visual function under well-defined physical constraints.

physics.med-ph↗

arXiv2026

Proximal State Nudging: Reducing Skill Atrophy from AI Assistance

Skill atrophy, the gradual decline of human capability under AI assistance, poses a safety risk in shared-control of semi-autonomous systems, where operators may be unable to distinguish their own inputs from autonomous corrections. We propose Proximal State Nudging (PSN), a shared autonomy algorithm that jointly optimizes for skill development and task performance by nudging users toward states estimated to be most learnable. We first show that PSN outperforms existing shared autonomy baselines in balancing student improvement in unassisted reward with overall shared performance, using simulated students in the classic LunarLander environment. We then present, to the best of our knowledge, the first human subject studies of a planner incorporating learning-compatible shared autonomy: across two driving tasks in the CARLA simulator (High Performance Racing and Parallel Parking, n = 60), PSN produces up to 7x larger gains in unassisted skill than standard blended shared autonomy, while incurring 50% fewer collisions than unassisted self-practice.

cs.RO↗

arXiv2026

On the Strengths and Weaknesses of Data for Open-set Embodied Assistance

Embodied foundation models are increasingly performant in real-world domains such as robotics or autonomous driving. These models are often deployed in interactive or assistive settings, where it is important that these assistive models generalize to new users and new tasks. Diverse interactive data generation offers a promising avenue for providing data-efficient generalization capabilities for interactive embodied foundation models. In this paper, we investigate the generalization capabilities of a multimodal foundation model fine-tuned on diverse interactive assistance data in a synthetic domain. We explore generalization along two axes: a) assistance with unseen categories of user behavior and b) providing guidance in new configurations not encountered during training. We study a broad capability called \textbf{Open-Set Corrective Assistance}, in which the model needs to inspect lengthy user behavior and provide assistance through either corrective actions or language-based feedback. This task remains unsolved in prior work, which typically assumes closed corrective categories or relies on external planners, making it a challenging testbed for evaluating the limits of assistive data. To support this task, we generate synthetic assistive datasets in Overcooked and fine-tune a LLaMA-based model to evaluate generalization to novel tasks and user behaviors. Our approach provides key insights into the nature of assistive datasets required to enable open-set assistive intelligence. In particular, we show that performant models benefit from datasets that cover different aspects of assistance, including multimodal grounding, defect inference, and exposure to diverse scenarios.

cs.RO↗

arXiv2026

Test-retest Reliability of Psychophysical Tasks Using Structured Light-Induced Entoptic Phenomena

Structured light (SL)-induced polarization perception presents a promising method for the early detection of macular diseases such as age-related macular degeneration. We investigated the test-retest reliability of a psychophysical task using SL-based stimuli to induce entoptic patterns in individuals with healthy vision. Twenty-eight participants underwent thorough eye examinations to confirm they had healthy eyes and good vision (logMAR BCVA ~0.00). Of these, 25 participants (n=50 eyes) aged 21 to 75 completed two identical tasks separated by 1 to 14 days. Using SL-based stimuli that produced a rotating entoptic pattern containing 22 azimuthal brushes, we measured the retinal eccentricity threshold $R_T$ at which participants could reliably identify the direction of rotation by varying the size of a central obstruction. This threshold reflects the visual angle of the pattern for each participant. We calculated the reliability coefficient (intraclass correlation coefficient, ICC) using a two-way mixed-effects model and conducted a Bland-Altman analysis to assess test-retest reliability. The ICC was 0.83 [95% CI: 0.62 - 0.93] for right eyes (RE) and 0.93 [95% CI: 0.84 - 0.97] for left eyes (LE), indicating good reliability. The Bland-Altman analysis showed a mean difference of -0.32{\deg} (SD: 1.51) and -0.12{\deg} (SD: 1.03) for RE and LE respectively between the first and second sessions, with limits of agreement ranging from -3.28{\deg} to 2.64{\deg} and -2.14{\deg} to 1.89{\deg} for RE and LE respectively, confirming strong agreement and no significant bias. These results demonstrate that SL-based psychophysical tasks are a reliable method for assessing polarization perception, potentially improving screening for diseases affecting macular health.

physics.med-ph↗

arXiv2025

SAFe-Copilot: Unified Shared Autonomy Framework

Autonomous driving systems remain brittle in rare, ambiguous, and out-of-distribution scenarios, where human driver succeed through contextual reasoning. Shared autonomy has emerged as a promising approach to mitigate such failures by incorporating human input when autonomy is uncertain. However, most existing methods restrict arbitration to low-level trajectories, which represent only geometric paths and therefore fail to preserve the underlying driving intent. We propose a unified shared autonomy framework that integrates human input and autonomous planners at a higher level of abstraction. Our method leverages Vision Language Models (VLMs) to infer driver intent from multi-modal cues -- such as driver actions and environmental context -- and to synthesize coherent strategies that mediate between human and autonomous control. We first study the framework in a mock-human setting, where it achieves perfect recall alongside high accuracy and precision. A human-subject survey further shows strong alignment, with participants agreeing with arbitration outcomes in 92% of cases. Finally, evaluation on the Bench2Drive benchmark demonstrates a substantial reduction in collision rate and improvement in overall performance compared to pure autonomy. Arbitration at the level of semantic, language-based representations emerges as a design principle for shared autonomy, enabling systems to exercise common-sense reasoning and maintain continuity with human intent.

cs.RO↗

arXiv2025

ReGen: Generative Robot Simulation via Inverse Design

Simulation plays a key role in scaling robot learning and validating policies, but constructing simulations remains a labor-intensive process. This paper introduces ReGen, a generative simulation framework that automates simulation design via inverse design. Given a robot's behavior -- such as a motion trajectory or an objective function -- and its textual description, ReGen infers plausible scenarios and environments that could have caused the behavior. ReGen leverages large language models to synthesize scenarios by expanding a directed graph that encodes cause-and-effect relationships, relevant entities, and their properties. This structured graph is then translated into a symbolic program, which configures and executes a robot simulation environment. Our framework supports (i) augmenting simulations based on ego-agent behaviors, (ii) controllable, counterfactual scenario generation, (iii) reasoning about agent cognition and mental states, and (iv) reasoning with distinct sensing modalities, such as braking due to faulty GPS signals. We demonstrate ReGen in autonomous driving and robot manipulation tasks, generating more diverse, complex simulated environments compared to existing simulations with high success rates, and enabling controllable generation for corner cases. This approach enhances the validation of robot policies and supports data or simulation augmentation, advancing scalable robot learning for improved generalization and robustness. We provide code and example videos at: https://regen-sim.github.io/

cs.RO↗

arXiv2025

SimCoachCorpus: A naturalistic dataset with language and trajectories for embodied teaching

High-quality curated datasets are essential for training and evaluating AI approaches, but are often lacking in embodied interactive domains where language and physical action are intertwined. In particular, few datasets capture how people acquire motor skills in embodied tasks through verbal instruction over time. To address this gap, we introduce SimCoachCorpus: a unique dataset of race car simulator driving that enables the investigation of rich phenomena during guided and unguided motor skill acquisition. In this dataset, 29 humans were asked to drive in a driving simulator around a race track for approximately ninety minutes. Fifteen participants received one-on-one instruction from a professional performance driving coach, and 14 participants drove without coaching instruction. SimCoachCorpus includes features such as vehicle state and inputs, map (track boundaries and race-line), and cone landmarks. Additionally, these are synchronized with the coach's concurrent verbal feedback and additional terminal feedback at the end of each lap. We also provide high-quality annotations of high-level coaching categories for each concurrent feedback utterance, ratings on students' compliance with coaching advice, and self-reported cognitive load and emotional state of participants (gathered from surveys during the study). The final dataset includes over 20,000 concurrent feedback utterances, over 400 terminal feedback utterances, and over 40 hours of interactive driving data. Our naturalistic interactive dataset can be used to investigate motor learning dynamics, explore linguistic phenomena, and train computational models of teaching and learning. We demonstrate applications of this dataset for in-context learning, imitation learning, and topic modeling. Data is hosted at https://doi.org/10.7910/DVN/W7VTKZ and code is available at https://github.com/ToyotaResearchInstitute/sim_coach_corpus

cs.RO↗

arXiv2025

Timing the Message: Language-Based Notifications for Time-Critical Assistive Settings

In time-critical settings such as assistive driving, assistants often rely on alerts or haptic signals to prompt rapid human attention, but these cues usually leave humans to interpret situations and decide responses independently, introducing potential delays or ambiguity in meaning. Language-based assistive systems can instead provide instructions backed by context, offering more informative guidance. However, current approaches (e.g., social assistive robots) largely prioritize content generation while overlooking critical timing factors such as verbal conveyance duration, human comprehension delays, and subsequent follow-through duration. These timing considerations are crucial in time-critical settings, where even minor delays can substantially affect outcomes. We aim to study this inherent trade-off between timeliness and informativeness by framing the challenge as a sequential decision-making problem using an augmented-state Markov Decision Process. We design a framework combining reinforcement learning and a generated offline taxonomy dataset, where we balance the trade-off while enabling a scalable taxonomy dataset generation pipeline. Empirical evaluation with synthetic humans shows our framework improves success rates by over 40% compared to methods that ignore time delays, while effectively balancing timeliness and informativeness. It also exposes an often-overlooked trade-off between these two factors, opening new directions for optimizing communication in time-critical human-AI assistance.

cs.RO↗

arXiv2025

Towards Balancing Preference and Performance through Adaptive Personalized Explainability

As robots and digital assistants are deployed in the real world, these agents must be able to communicate their decision-making criteria to build trust, improve human-robot teaming, and enable collaboration. While the field of explainable artificial intelligence (xAI) has made great strides to enable such communication, these advances often assume that one xAI approach is ideally suited to each problem (e.g., decision trees to explain how to triage patients in an emergency or feature-importance maps to explain radiology reports). This fails to recognize that users have diverse experiences or preferences for interaction modalities. In this work, we present two user-studies set in a simulated autonomous vehicle (AV) domain. We investigate (1) population-level preferences for xAI and (2) personalization strategies for providing robot explanations. We find significant differences between xAI modes (language explanations, feature-importance maps, and decision trees) in both preference (p < 0.01) and performance (p < 0.05). We also observe that a participant's preferences do not always align with their performance, motivating our development of an adaptive personalization strategy to balance the two. We show that this strategy yields significant performance gains (p < 0.05), and we conclude with a discussion of our findings and implications for xAI in human-robot interactions.

cs.HC↗

arXiv2025

Shared Autonomy for Proximal Teaching

Motor skill learning often requires experienced professionals who can provide personalized instruction. Unfortunately, the availability of high-quality training can be limited for specialized tasks, such as high performance racing. Several recent works have leveraged AI-assistance to improve instruction of tasks ranging from rehabilitation to surgical robot tele-operation. However, these works often make simplifying assumptions on the student learning process, and fail to model how a teacher's assistance interacts with different individuals' abilities when determining optimal teaching strategies. Inspired by the idea of scaffolding from educational psychology, we leverage shared autonomy, a framework for combining user inputs with robot autonomy, to aid with curriculum design. Our key insight is that the way a student's behavior improves in the presence of assistance from an autonomous agent can highlight which sub-skills might be most ``learnable'' for the student, or within their Zone of Proximal Development. We use this to design Z-COACH, a method for using shared autonomy to provide personalized instruction targeting interpretable task sub-skills. In a user study (n=50), where we teach high performance racing in a simulated environment of the Thunderhill Raceway Park with the CARLA Autonomous Driving simulator, we show that Z-COACH helps identify which skills each student should first practice, leading to an overall improvement in driving time, behavior, and smoothness. Our work shows that increasingly available semi-autonomous capabilities (e.g. in vehicles, robots) can not only assist human users, but also help *teach* them.

cs.RO↗

arXiv2024

Eye dominance and testing order effects in the circularly-oriented macular pigment optical density measurements that rely on the perception of structured light-based stimuli

Psychophysical discrimination of structured light (SL) stimuli may be useful in screening for various macular disorders, including degenerative macular diseases. The circularly-oriented macular pigment optical density (coMPOD), calculated from the discrimination performance of SL-induced entoptic phenomena, may reveal a novel functional biomarker of macular health. In this study, we investigated the potential influence of eye dominance and testing order effects on SL-based stimulus perception, factors that potentially influence the sensitivity of screening tests based on SL technology. A total of 28 participants (aged 18-38 years) were selected for the study after undergoing a comprehensive eye examination. A psychophysical task was performed where various SL-based entoptic images with multiple azimuthal fringes rotating with a specific temporal frequency were projected onto the participants' retinas. By occluding the central areas of entoptic images, we measured the retinal eccentricity ($R$) of the perceivable area of the stimuli. The slope of the coMPOD profile ($a$-value) was calculated for each participant using a spatiotemporal sensitivity model that takes into account the perceptual threshold measurements of structured light stimuli with varying spatial densities and temporal frequencies. The Pearson correlation coefficient between eye dominance and testing order effects was $r=0.8$ ($p<0.01$). The Bland-Altman plots for both factors indicated zero bias. The results indicate repeatable measurements for both eyes, implying minimal impact from eye dominance and testing order on SL-based stimulus perception. The results provide a foundation for future studies exploring the clinical utility of SL tools in eye health.

q-bio.NC↗

arXiv2024

Dreaming to Assist: Learning to Align with Human Objectives for Shared Control in High-Speed Racing

Tight coordination is required for effective human-robot teams in domains involving fast dynamics and tactical decisions, such as multi-car racing. In such settings, robot teammates must react to cues of a human teammate's tactical objective to assist in a way that is consistent with the objective (e.g., navigating left or right around an obstacle). To address this challenge, we present Dream2Assist, a framework that combines a rich world model able to infer human objectives and value functions, and an assistive agent that provides appropriate expert assistance to a given human teammate. Our approach builds on a recurrent state space model to explicitly infer human intents, enabling the assistive agent to select actions that align with the human and enabling a fluid teaming interaction. We demonstrate our approach in a high-speed racing domain with a population of synthetic human drivers pursuing mutually exclusive objectives, such as "stay-behind" and "overtake". We show that the combined human-robot team, when blending its actions with those of the human, outperforms the synthetic humans alone as well as several baseline assistance strategies, and that intent-conditioning enables adherence to human preferences during task execution, leading to improved performance while satisfying the human's objective.

cs.RO↗

arXiv2023

Interpretable Reinforcement Learning for Robotics and Continuous Control

Interpretability in machine learning is critical for the safe deployment of learned policies across legally-regulated and safety-critical domains. While gradient-based approaches in reinforcement learning have achieved tremendous success in learning policies for continuous control problems such as robotics and autonomous driving, the lack of interpretability is a fundamental barrier to adoption. We propose Interpretable Continuous Control Trees (ICCTs), a tree-based model that can be optimized via modern, gradient-based, reinforcement learning approaches to produce high-performing, interpretable policies. The key to our approach is a procedure for allowing direct optimization in a sparse decision-tree-like representation. We validate ICCTs against baselines across six domains, showing that ICCTs are capable of learning policies that parity or outperform baselines by up to 33% in autonomous driving scenarios while achieving a 300x-600x reduction in the number of parameters against deep learning baselines. We prove that ICCTs can serve as universal function approximators and display analytically that ICCTs can be verified in linear time. Furthermore, we deploy ICCTs in two realistic driving domains, based on interstate Highway-94 and 280 in the US. Finally, we verify ICCT's utility with end-users and find that ICCTs are rated easier to simulate, quicker to validate, and more interpretable than neural networks.

cs.RO↗

arXiv2022

FedPC: Federated Learning for Language Generation with Personal and Context Preference Embeddings

Federated learning is a training paradigm that learns from multiple distributed users without aggregating data on a centralized server. Such a paradigm promises the ability to deploy machine-learning at-scale to a diverse population of end-users without first collecting a large, labeled dataset for all possible tasks. As federated learning typically averages learning updates across a decentralized population, there is a growing need for personalization of federated learning systems (i.e conversational agents must be able to personalize to a specific user's preferences). In this work, we propose a new direction for personalization research within federated learning, leveraging both personal embeddings and shared context embeddings. We also present an approach to predict these ``preference'' embeddings, enabling personalization without backpropagation. Compared to state-of-the-art personalization baselines, our approach achieves a 50\% improvement in test-time perplexity using 0.001\% of the memory required by baseline approaches, and achieving greater sample- and compute-efficiency.

cs.CL↗

arXiv2022

Multi-UAV Planning for Cooperative Wildfire Coverage and Tracking with Quality-of-Service Guarantees

In recent years, teams of robot and Unmanned Aerial Vehicles (UAVs) have been commissioned by researchers to enable accurate, online wildfire coverage and tracking. While the majority of prior work focuses on the coordination and control of such multi-robot systems, to date, these UAV teams have not been given the ability to reason about a fire's track (i.e., location and propagation dynamics) to provide performance guarantee over a time horizon. Motivated by the problem of aerial wildfire monitoring, we propose a predictive framework which enables cooperation in multi-UAV teams towards collaborative field coverage and fire tracking with probabilistic performance guarantee. Our approach enables UAVs to infer the latent fire propagation dynamics for time-extended coordination in safety-critical conditions. We derive a set of novel, analytical temporal, and tracking-error bounds to enable the UAV-team to distribute their limited resources and cover the entire fire area according to the case-specific estimated states and provide a probabilistic performance guarantee. Our results are not limited to the aerial wildfire monitoring case-study and are generally applicable to problems, such as search-and-rescue, target tracking and border patrol. We evaluate our approach in simulation and provide demonstrations of the proposed framework on a physical multi-robot testbed to account for real robot dynamics and restrictions. Our quantitative evaluations validate the performance of our method accumulating 7.5x and 9.0x smaller tracking-error than state-of-the-art model-based and reinforcement learning benchmarks, respectively.

cs.RO↗

arXiv2022

FedEmbed: Personalized Private Federated Learning

Federated learning enables the deployment of machine learning to problems for which centralized data collection is impractical. Adding differential privacy guarantees bounds on privacy while data are contributed to a global model. Adding personalization to federated learning introduces new challenges as we must account for preferences of individual users, where a data sample could have conflicting labels because one sub-population of users might view an input positively, but other sub-populations view the same input negatively. We present FedEmbed, a new approach to private federated learning for personalizing a global model that uses (1) sub-populations of similar users, and (2) personal embeddings. We demonstrate that current approaches to federated learning are inadequate for handling data with conflicting labels, and we show that FedEmbed achieves up to 45% improvement over baseline approaches to personalized private federated learning.

cs.LG↗

arXiv2022

Learning Interpretable, High-Performing Policies for Autonomous Driving

Gradient-based approaches in reinforcement learning (RL) have achieved tremendous success in learning policies for autonomous vehicles. While the performance of these approaches warrants real-world adoption, these policies lack interpretability, limiting deployability in the safety-critical and legally-regulated domain of autonomous driving (AD). AD requires interpretable and verifiable control policies that maintain high performance. We propose Interpretable Continuous Control Trees (ICCTs), a tree-based model that can be optimized via modern, gradient-based, RL approaches to produce high-performing, interpretable policies. The key to our approach is a procedure for allowing direct optimization in a sparse decision-tree-like representation. We validate ICCTs against baselines across six domains, showing that ICCTs are capable of learning interpretable policy representations that parity or outperform baselines by up to 33% in AD scenarios while achieving a 300x-600x reduction in the number of policy parameters against deep learning baselines. Furthermore, we demonstrate the interpretability and utility of our ICCTs through a 14-car physical robot demonstration.

cs.LG↗

arXiv2021

Multimodal Punctuation Prediction with Contextual Dropout

Automatic speech recognition (ASR) is widely used in consumer electronics. ASR greatly improves the utility and accessibility of technology, but usually the output is only word sequences without punctuation. This can result in ambiguity in inferring user-intent. We first present a transformer-based approach for punctuation prediction that achieves 8% improvement on the IWSLT 2012 TED Task, beating the previous state of the art [1]. We next describe our multimodal model that learns from both text and audio, which achieves 8% improvement over the text-only algorithm on an internal dataset for which we have both the audio and transcriptions. Finally, we present an approach to learning a model using contextual dropout that allows us to handle variable amounts of future context at test time.

cs.CL↗