arXiv subjects

Yankai Li

Publications and source records attributed to Yankai Li.

7 recordsLinked to original sources

arXiv2026

Implicit Maximum Likelihood Estimation for Real-time Generative Model Predictive Control

Diffusion-based models have recently shown strong performance in trajectory planning, as they are capable of capturing diverse, multimodal distributions of complex behaviors. A key limitation of these models is their slow inference speed, which results from the iterative denoising process. This makes them less suitable for real-time applications such as closed-loop model predictive control (MPC), where plans must be generated quickly and adapted continuously to a changing environment. In this paper, we investigate Implicit Maximum Likelihood Estimation (IMLE) as an alternative generative modeling approach for planning. IMLE offers strong mode coverage while enabling inference that is two orders of magnitude faster, making it particularly well suited for real-time MPC tasks. Our results demonstrate that IMLE achieves competitive performance on standard offline reinforcement learning benchmarks compared to the standard diffusion-based planner, while substantially improving planning speed in both open-loop and closed-loop settings. We further validate IMLE in a closed-loop human navigation scenario, operating in real-time, demonstrating how it enables rapid and adaptive plan generation in dynamic environments. Real-world videos and code are available at https://gmpc-imle.github.io/.

cs.RO↗

arXiv2025

FLASH Viterbi: Fast and Adaptive Viterbi Decoding for Modern Data Systems

The Viterbi algorithm is a key operator for structured sequence inference in modern data systems, with applications in trajectory analysis, online recommendation, and speech recognition. As these workloads increasingly migrate to resource-constrained edge platforms, standard Viterbi decoding remains memory-intensive and computationally inflexible. Existing methods typically trade decoding time for space efficiency, but often incur significant runtime overhead and lack adaptability to various system constraints. This paper presents FLASH Viterbi, a Fast, Lightweight, Adaptive, and Hardware-Friendly Viterbi decoding operator that enhances adaptability and resource efficiency. FLASH Viterbi combines a non-recursive divide-and-conquer strategy with pruning and parallelization techniques to enhance both time and memory efficiency, making it well-suited for resource-constrained data systems. To further decouple space complexity from the hidden state space size, we present FLASH-BS Viterbi, a dynamic beam search variant built on a memory-efficient data structure. Both proposed algorithms exhibit strong adaptivity to diverse deployment scenarios by dynamically tuning internal parameters. To ensure practical deployment on edge devices, we also develop FPGA-based hardware accelerators for both algorithms, demonstrating high throughput and low resource usage. Extensive experiments show that our algorithms consistently outperform existing baselines in both decoding time and memory efficiency, while preserving adaptability and hardware-friendly characteristics essential for modern data systems. All codes are publicly available at https://github.com/Dzh-16/FLASH-Viterbi.

cs.DC↗

arXiv2025

HJRNO: Hamilton-Jacobi Reachability with Neural Operators

Ensuring the safety of autonomous systems under uncertainty is a critical challenge. Hamilton-Jacobi reachability (HJR) analysis is a widely used method for guaranteeing safety under worst-case disturbances. In this work, we propose HJRNO, a neural operator-based framework for solving backward reachable tubes (BRTs) efficiently and accurately. By leveraging neural operators, HJRNO learns a mapping between value functions, enabling fast inference with strong generalization across different obstacle shapes and system configurations. We demonstrate that HJRNO achieves low error on random obstacle scenarios and generalizes effectively across varying system dynamics. These results suggest that HJRNO offers a promising foundation model approach for scalable, real-time safety analysis in autonomous systems.

cs.RO↗

arXiv2025

Unifying Model Predictive Path Integral Control, Reinforcement Learning, and Diffusion Models for Optimal Control and Planning

Model Predictive Path Integral (MPPI) control, Reinforcement Learning (RL), and Diffusion Models have each demonstrated strong performance in trajectory optimization, decision-making, and motion planning. However, these approaches have traditionally been treated as distinct methodologies with separate optimization frameworks. In this work, we establish a unified perspective that connects MPPI, RL, and Diffusion Models through gradient-based optimization on the Gibbs measure. We first show that MPPI can be interpreted as performing gradient ascent on a smoothed energy function. We then demonstrate that Policy Gradient methods reduce to MPPI by applying an exponential transformation to the objective function. Additionally, we establish that the reverse sampling process in diffusion models follows the same update rule as MPPI.

cs.LG↗

arXiv2023

Filled Colloidal Gel Rheology: Strengthening, Stiffening, and Tunability

Filler-induced strengthening is ubiquitous in materials science and is particularly well-established in polymeric nanocomposites. Despite having similar constituents, colloidal gels with solid filling exhibit distinct rheology, which is of practical interest to industry (e.g., lithium-ion batteries) yet remains poorly understood. We show, using experiments and simulations, that filling monotonically enhances the yield stress (i.e., strength) of colloidal gels while the elastic modulus (i.e., stiffness) first increases and then decreases. The latter softening effect results from a frustrated gel matrix at dense filling, evidenced by a growing inter-phase pressure. This structural frustration is, however, not detrimental to yielding resistance. Instead, fillers offer additional mechanical support to the gel backbone via percolating force chains, decreasing the yield strain at the same time. We develop a mechanistic picture of this phenomenology that leads us to a novel `filler-removal protocol,' making individual control over the strength and brittleness of a composite gel possible.

cond-mat.soft↗

arXiv2023

ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networks

Traffic data serves as a fundamental component in both research and applications within intelligent transportation systems. However, real-world transportation data, collected from loop detectors or similar sources, often contains missing values (MVs), which can adversely impact associated applications and research. Instead of discarding this incomplete data, researchers have sought to recover these missing values through numerical statistics, tensor decomposition, and deep learning techniques. In this paper, we propose an innovative deep learning approach for imputing missing data. A graph attention architecture is employed to capture the spatial correlations present in traffic data, while a bidirectional neural network is utilized to learn temporal information. Experimental results indicate that our proposed method outperforms all other benchmark techniques, thus demonstrating its effectiveness.

cs.LG↗

arXiv2022

Impact of granular inclusions on the phase behavior of colloidal gels

Colloidal gels formed from small attractive particles are commonly used in formulations to keep larger components in suspension. However, despite extensive work characterizing unfilled gels, little is known about how larger inclusions alter the phase behavior and microstructure of the colloidal system. Here we use numerical simulations to examine how larger `granular' particles can alter the gel transition phase boundaries. We find two distinct regimes depending on both the filler size and native gel structure: a `passive' regime where the filler fits into already-present voids, giving little change in the transition, and an `active' regime where the filler no longer fits in these voids and instead perturbs the native structure. In this second regime the phase boundary is controlled by an effective colloidal volume fraction given by the available free volume.

cond-mat.soft↗