arXiv subjects

Yingke Li

Publications and source records attributed to Yingke Li.

15 recordsLinked to original sources

arXiv2026

Interleaved POMDP Planning for Multi-Object Search in Unknown Multi-Room Household Environments

Multi-object search in unknown household environments requires planning under extensive uncertainty - from unknown object locations to cluttered spaces with unobserved obstacles. POMDPs offer a principled framework for such problems but remain intractable in large domains. We propose Inter-POMDP, a novel interleaved POMDP planning algorithm that decomposes this challenge into two interacting levels: a high-level POUCT planner reasons over object distributions using LLM-informed histogram beliefs, while a low-level motion planner models navigation uncertainty with obstacle-aware particle beliefs as domain knowledge to guide high-level POUCT. This interleaved design balances planning quality and efficiency despite the large search space across unknown multi-room environments. Both simulation and real-world experiments show that our Inter-POMDP algorithm reduces collision counts by up to 63%, navigation steps by up to 35%, and detection counts by up to 32% compared with baseline methods. Full videos are https://sites.google.com/view/inter-pomdp

cs.RO↗

arXiv2026

On-Line Policy Iteration with Trajectory-Driven Policy Generation

We consider deterministic finite-horizon optimal control problems with a fixed initial state. We introduce an on-line policy iteration method, which, starting from a given policy, however obtained, generates a sequence of cost-improving policies and corresponding trajectories. Each policy produces a trajectory, which is used in turn to generate data for training the next policy. The method is motivated by problems that are repeatedly solved starting from the same initial state, including discrete optimization and path planning for repetitive tasks. For such problems, the method is fast enough to be used on-line. Under a natural consistency condition, we show that the sequence of costs of the generated policies is monotonically improving for the given initial state (but not necessarily for other states). We illustrate our results with computational studies from combinatorial optimization and 3-dimensional path planning for drones {and a robot arm} in the presence of obstacles. We also discuss briefly a stochastic counterpart of our algorithm. Our proposed framework combines elements of rollout and policy iteration with flexible trajectory-based policy representations, and applies to problems involving a single as well as multiple decision makers. It also provides a principled way to train neural network-based policies using trajectory data, while preserving monotonic cost improvement.

eess.SY↗

arXiv2026

Simulation Based Characterization of Deconvolution-Based PMT Waveform Reconstruction Under Large Charge Dynamic Range and Varying Scintillation Time Profiles

Photomultiplier tubes (PMTs) are widely used as photon sensors for neutrino and dark matter detection. Accurate charge and time information extracted from PMT waveforms is crucial for event reconstruction. An algorithm based on deconvolution technology was proposed and applied to the reconstruction of PMT waveforms. This study further investigated the reliability of the deconvolution algorithm when handling a large charge dynamic range (0-200 photoelectrons), varying scintillation time profiles, and muon-induced large signals. Monte Carlo data confirmed that the deconvolution algorithm exhibits relatively stable reconstruction performance: under the simulation conditions described in this paper (including a noise level of 0.1 PE, single photoelectron charge resolution of 30%, 1 GHz sampling rate, 1000 ns window, three undershoot configurations, and eight scintillation time profiles), the residual non-linearity of charge reconstruction is controlled to approximately 1% over the range of 0 to 200 photoelectrons, and the algorithm is capable of handling muon-induced large signals. The reconstruction performance depends on adequate baseline recovery; a waveform window that is too short relative to the undershoot tail leads to degraded reconstruction quality, which can be mitigated by extending the sampling window.

physics.ins-det↗

arXiv2026

SEED-SET: Scalable Evolving Experimental Design for System-level Ethical Testing

As autonomous systems such as drones, become increasingly deployed in high-stakes, human-centric domains, it is critical to evaluate the ethical alignment since failure to do so imposes imminent danger to human lives, and long term bias in decision-making. Automated ethical benchmarking of these systems is understudied due to the lack of ubiquitous, well-defined metrics for evaluation, and stakeholder-specific subjectivity, which cannot be modeled analytically. To address these challenges, we propose SEED-SET, a Bayesian experimental design framework that incorporates domain-specific objective evaluations, and subjective value judgments from stakeholders. SEED-SET models both evaluation types separately with hierarchical Gaussian Processes, and uses a novel acquisition strategy to propose interesting test candidates based on learnt qualitative preferences and objectives that align with the stakeholder preferences. We validate our approach for ethical benchmarking of autonomous agents on two applications and find our method to perform the best. Our method provides an interpretable and efficient trade-off between exploration and exploitation, by generating up to $2\times$ optimal test candidates compared to baselines, with $1.25\times$ improvement in coverage of high dimensional search spaces.

cs.AI↗

arXiv2026

Curiosity is Knowledge: Self-Consistent Learning and No-Regret Optimization with Active Inference

Active inference (AIF) unifies exploration and exploitation by minimizing the Expected Free Energy (EFE), balancing epistemic value (information gain) and pragmatic value (task performance) through a curiosity coefficient. Yet it has been unclear when this balance yields both coherent learning and efficient decision-making: insufficient curiosity can drive myopic exploitation and prevent uncertainty resolution, while excessive curiosity can induce unnecessary exploration and regret. We establish the first theoretical guarantee for EFE-minimizing agents, showing that a single requirement--sufficient curiosity--simultaneously ensures self-consistent learning (Bayesian posterior consistency) and no-regret optimization (bounded cumulative regret). Our analysis characterizes how this mechanism depends on initial uncertainty, identifiability, and objective alignment, thereby connecting AIF to classical Bayesian experimental design and Bayesian optimization within one theoretical framework. We further translate these theories into practical design guidelines for tuning the epistemic-pragmatic trade-off in hybrid learning-optimization problems, validated through real-world experiments.

cs.LG↗

arXiv2026

Pragmatic Curiosity: A Unified Framework for Hybrid Learning and Optimization via Active Inference

Many engineering and scientific workflows rely on expensive black-box evaluations, requiring sequential decisions that must both improve task performance and reduce uncertainty. Bayesian optimization (BO) and Bayesian experimental design (BED) provide powerful but largely separate treatments of goal-directed optimization and information-seeking experimentation, leaving limited guidance for hybrid settings in which learning and optimization are intrinsically coupled. We propose Pragmatic Curiosity (PraC), a unified framework for hybrid learning and optimization via active inference. PraC evaluates candidate queries by trading information gain about a task-relevant latent symbol against an expected regret-based potential over outcomes. This formulation exposes three operational design choices: which latent quantity should be clarified, how task value is encoded as regret, and how strongly information gain should be exchanged against pragmatic value. We instantiate PraC across three regimes of increasing complexity: decision-oriented plume monitoring with fixed global symbols and known downstream losses, targeted active search with induced local symbols and evolving coverage goals, and composite Bayesian optimization with hierarchical regret learning under unknown preferences. Across these regimes, PraC reduces downstream decision risk, improves coverage of critical outcome regions, and jointly learns predictive and preference structures without relying on task-specific staging rules.

cs.LG↗

arXiv2025

Bayesian Risk-averse Model Predictive Control with Consistency and Stability Guarantees

Model Predictive Control (MPC) is a powerful framework for constrained control, but its performance and safety can be severely degraded when the prediction model is learned online and thus remains uncertain. In this work, we develop a Bayesian risk-averse MPC framework for stochastic, discrete-time, nonlinear systems that provides theoretical guarantees on the consistency of Bayesian learning and closed-loop stability. First, we study Bayesian learning under the conditionally independent state transitions induced by feedback control and establish explicit conditions for Bayesian consistency on an infinitely countable parameter space. Second, we introduce a general notion of risk-averse asymptotic stability (RAAS), defined via comparison function classes and independent of any specific coherent risk measure or convergence rate, and we derive a risk-averse Lyapunov stability theorem together with MPC-specific stability conditions. Third, building on these foundations, we design a practical Bayesian risk-averse MPC scheme that separates epistemic (parametric) and aleatoric (disturbance) uncertainty: additive disturbances are treated in a risk-neutral fashion, while parametric uncertainty is managed via dynamically shrinking ambiguity sets constructed from Bayesian credible intervals, approximated online using particle filtering. To enable real-time implementation, we propose both an optimal and a sub-optimal receding-horizon control policy, the latter obtained by warm-starting from the previous solution, and prove that asymptotic RAAS is recovered as the Bayesian estimator becomes consistent.

math.OC↗

arXiv2024

Film Thickness Gauge Based on Interferometric Principle of Y-shaped Optical Fiber

In this paper, a thin film thickness gauge based on the interferometric principle of Y-shaped optical fiber is proposed to achieve accurate measurement of film thickness. In this paper, the optical fiber, the interferometric principle and the film thickness calculation principle are introduced, and the interferometric thickness measurement system based on Y-shaped optical fiber is constructed. The system uses the special structure of Y-shaped optical fiber to transmit the optical signal generated by the light source to the surface of the thin film, and obtains coherent optical signals of different wavelengths through reflection and interference. The spectrometer is used to receive and interpret these interference signals, and the thickness of the film is calculated according to the wavelength difference of the peak positions of the adjacent stages, combined with the refractive index of the film. In the specific design, the paper elaborates on the design of each part of the instrument, including the selection and parameter setting of the light source, Y-fiber and spectrometer. Among them, the Y-shaped optical fiber, as the core component of the instrument, has the function of transmitting optical signals and detecting optical signals on the surface of thin films. At the same time, the paper also introduces the housing packaging and internal assembly process of the instrument to ensure the portability and stability of the instrument. The results show that the thickness gauge has high measurement accuracy and stability, which can meet the needs of practical applications.

physics.optics↗

arXiv2024

Multifunctional Portable Optical Measuring Instrument Based on Y-Fiber Optics

Based on grating diffraction principle, optical fiber transmission principle and optical interference principle, a multi-functional portable optical measuring instrument is constructed in this paper. The optical measurement visualization spectrometer based on CCD photoelectric image sensor is designed and assembled. The "Y" optical signal transmission fiber optical path suitable for multi-function measurement is improved and designed. The multi-function optical measurement system is built by combining with remote controlled multi-color LED lights. The spectral analysis, solution concentration monitoring and film thickness measurement are realized. The experimental results show that the observable wavelength range of the spectrometer is about 340-1050nm and the resolution is 1nm. The solution concentration can be obtained by measuring absorbance with optical fiber spectrometer. The film thickness measuring instrument can accurately measure the thickness of the micron film, and the measurement accuracy can reach 1.25 {\mu}m. It is proved that the instrument integrates multiple functions, has high measurement accuracy and wide range, and realizes non-contact measurement.

physics.optics↗

arXiv2023

Trust-Preserved Human-Robot Shared Autonomy enabled by Bayesian Relational Event Modeling

Shared autonomy functions as a flexible framework that empowers robots to operate across a spectrum of autonomy levels, allowing for efficient task execution with minimal human oversight. However, humans might be intimidated by the autonomous decision-making capabilities of robots due to perceived risks and a lack of trust. This paper proposed a trust-preserved shared autonomy strategy that allows robots to seamlessly adjust their autonomy level, striving to optimize team performance and enhance their acceptance among human collaborators. By enhancing the relational event modeling framework with Bayesian learning techniques, this paper enables dynamic inference of human trust based solely on time-stamped relational events communicated within human-robot teams. Adopting a longitudinal perspective on trust development and calibration in human-robot teams, the proposed trust-preserved shared autonomy strategy warrants robots to actively establish, maintain, and repair human trust, rather than merely passively adapting to it. We validate the effectiveness of the proposed approach through a user study on a human-robot collaborative search and rescue scenario. The objective and subjective evaluations demonstrate its merits on both task execution and user acceptability over the baseline approach that does not consider the preservation of trust.

cs.RO↗

arXiv2020

Towards Safe Locomotion Navigation in Partially Observable Environments with Uneven Terrain

This study proposes an integrated task and motion planning method for dynamic locomotion in partially observable environments with multi-level safety guarantees. This layered planning framework is composed of a high-level symbolic task planner and a low-level phase-space motion planner. A belief abstraction at the task planning level enables belief estimation of dynamic obstacle locations and guarantees navigation safety with collision avoidance. The high-level task planner, i.e., a two-level navigation planner, employs linear temporal logic for a reactive game synthesis between the robot and its environment while incorporating low-level safe keyframe policies into formal task specification design. The synthesized task planner commands a series of locomotion actions including walking step length, step height, and heading angle changes, to the underlying keyframe decision-maker, which further determines the robot center-of-mass apex velocity keyframe. The low-level phase-space planner uses a reduced-order locomotion model to generate non-periodic trajectories meeting balancing safety criteria for straight and steering walking. These criteria are characterized by constraints on locomotion keyframe states, and are used to define keyframe transition policies via viability kernels. Simulation results of a Cassie bipedal robot designed by Agility Robotics demonstrate locomotion maneuvering in a three-dimensional, partially observable environment consisting of dynamic obstacles and uneven terrain.

cs.RO↗

arXiv2019

Testing Shear Recovery with Field Distortion

The tilt, rotation, or offset of each CCD with respect to the focal plane, as well as the distortion of the focal plane itself, cause shape distortions to the observed objects, an effect typically known as field distortion (FD). We point out that FD provides a unique way of quantifying the accuracy of cosmic shear measurement. The idea is to stack the shear estimators from galaxies that share similar FD-induced shape distortions. Given that the latter can be calculated with parameters from astrometric calibrations, the accuracy of the shear estimator can be directly tested on real images. It provides a way to calibrate the multiplicative and additive shear recovery biases within the scientific data itself, without requiring simulations or any external data sets. We use the CFHTLenS images to demonstrate the accuracy of the Fourier_Quad shear recovery method. We highlight some details in our image processing pipeline, including background removal, source identification and deblending, astrometric calibration, star selection for PSF reconstruction, noise reduction, etc.. We show that in the shear ranges of -0.005 < g_1 < 0.005 and -0.008 < g_2 < 0.008, the multiplicative biases are at the level of < 0.04. Slight additive biases on the order of 5E-4 (6 sigma) are identified for sources provided by the official CFHTLenS catalog (not using its shear catalog), but are minor (4 sigma) for source catalog generated by our Fourier_Quad pipeline.

astro-ph.CO↗

arXiv2017

Removing the Impact of Correlated PSF Uncertainties in Weak Lensing

Accurate reconstruction of the spatial distributions of the Point Spread Function (PSF) is crucial for high precision cosmic shear measurements. Nevertheless, current methods are not good at recovering the PSF fluctuations of high spatial frequencies. In general, the residual PSF fluctuations are spatially correlated, therefore can significantly contaminate the correlation functions of the weak lensing signals. We propose a method to correct for this contamination statistically, without any assumptions on the PSF and galaxy morphologies or their spatial distribution. We demonstrate our idea with the data from the W2 field of CFHTLenS.

astro-ph.CO↗

arXiv2017

An accurate centroid algorithm for PSF reconstruction

In this work, we present a novel centroiding method based on Fourier space Phase Fitting(FPF) for Point Spread Function(PSF) reconstruction. We generate two sets of simulations to test our method. The first set is generated by GalSim with elliptical Moffat profile and strong anisotropy which shifts the center of the PSF. The second set of simulation is drawn from CFHT i band stellar imaging data. We find non-negligible anisotropy from CFHT stellar images, which leads to $\sim$0.08 scatter in unit of pixels using polynomial fitting method Vakili and Hogg (2016). And we apply FPF method to estimate the centroid in real space, this scatter reduces to $\sim$0.04 in SNR=200 CFHT like sample. In low SNR (50 and 100) CFHT like samples, the background noise dominates the shifting of the centroid, therefore the scatter estimated from different methods are similar. We compare polynomial fitting and FPF using GalSim simulation with optical anisotropy. We find that in all SNR$\sim$50, 100 and 200) samples, FPF performs better than polynomial fitting by a factor of $\sim$3. In general, we suggest that in real observations there are anisotropy which shift the centroid, and FPF method is a better way to accurately locate it.

astro-ph.IM↗

arXiv2017

Testing PSF Interpolation In Weak Lensing With Real Data

Reconstruction of the point spread function (PSF) is a critical process in weak lensing measurement. We develop a real-data based and galaxy-oriented pipeline to compare the performances of various PSF reconstruction schemes. Making use of a large amount of the CFHTLenS data, the performances of three classes of interpolating schemes - polynomial, Kriging, and Shepard - are evaluated. We find that polynomial interpolations with optimal orders and domains perform the best. We quantify the effect of the residual PSF reconstruction error on shear recovery in terms of the multiplicative and additive biases, and their spatial correlations using the shear measurement method of Zhang et al. (2015). We find that the impact of PSF reconstruction uncertainty on the shear-shear correlation can be significantly reduced by cross correlating the shear estimators from different exposures. It takes only 0.2 stars (SNR > 100) per square arcmin on each exposure to reach the best performance of PSF interpolation, a requirement that is generally satisfied in the CFHTlenS data.

astro-ph.CO↗