arXiv subjects

Sifan Chen

Publications and source records attributed to Sifan Chen.

3 recordsLinked to original sources

arXiv2026

PccDiffuser: Multi-solution Motion Planning for Continuum Robots

We present the PccDiffuser, a conditional diffusion framework for continuum robots that learns a multimodal distribution over complete configuration-space paths and samples multiple candidate solutions in parallel, which are subsequently converted into an executable trajectory by time allocation considering actuator constraints. Under the piecewise constant-curvature model, we use exponential co-ordinates to describe the robot kinematics, and use graph neural network to encode a variable number of environment obstacles. Analytical differential kinematics is incorporated in the denoising process to improve terminal accuracy and whole-body clearance. On a mixed test set comprising workspace with zero to four obstacles, PccDiffuser achieved a success rate of 91\%. Compared with existing sampling- and optimisation-based benchmarks, it delivered both a higher success rate and greater computational efficiency, with the latter advantage becoming more substantial when sampling more candidate solutions. Experiments on a three-section tendon-driven continuum robot further demonstrate consecutive planning, multi-solution planning, and whole-body obstacle avoidance.

cs.RO↗

arXiv2026

UTune: Towards Uncertainty-Aware Online Index Tuning

There have been a flurry of recent proposals on learned benefit estimators for index tuning. Although these learned estimators show promising improvement over what-if query optimizer calls in terms of the accuracy of estimated index benefit, they face significant limitations when applied to online index tuning, an arguably more common and more challenging scenario in real-world applications. There are two major challenges for learned index benefit estimators in online tuning: (1) limited amount of query execution feedback that can be used to train the models, and (2) constant coming of new unseen queries due to workload drifts. The combination of the two hinders the generalization capability of existing learned index benefit estimators. To overcome these challenges, we present UTune, an uncertainty-aware online index tuning framework that employs operator-level learned models with improved generalization over unseen queries. At the core of UTune is an uncertainty quantification mechanism that characterizes the inherent uncertainty of the operator-level learned models given limited online execution feedback. We further integrate uncertainty information into index selection and configuration enumeration, the key component of any index tuner, by developing a new variant of the classic $\epsilon$-greedy search strategy with uncertainty-weighted index benefits. Experimental evaluation shows that UTune not only significantly improves the workload execution time compared to state-of-the-art online index tuners but also reduces the index exploration overhead, resulting in faster convergence when the workload is relatively stable.

cs.DB↗

arXiv2025

A Quasi-Optimal Shape Design Method for Lattice Structure Construction

Lattice structures, known for their superior mechanical properties, are widely used in industries such as aerospace, automotive, and biomedical. Their advantages primarily lie in the interconnected struts at the micro-scale. The robust construction of these struts is crucial for downstream design and manufacturing applications, as it provides a detailed shape description necessary for precise simulation and fabrication. However, constructing lattice structures presents significant challenges, particularly at nodes where multiple struts intersect. The complexity of these intersections can lead to robustness issues. To address this challenge, this paper presents an optimization-based approach that simplifies the construction of lattice structures by cutting struts and connecting them to optimized node shapes. By utilizing the recent Grey Wolf optimization method -- a type of meta-heuristic method -- for node shape design, the approach ensures robust model construction and optimal shape design. Its effectiveness has been validated through a series of case studies with increasing topological and geometric complexity.

cs.CE↗