arXiv subjects

Yuto Tanaka

Publications and source records attributed to Yuto Tanaka.

4 recordsLinked to original sources

arXiv2026

Understanding Scattered Forest Search: A Version-Space Perspective on Multi-Turn Program Correction

In multi-turn program correction, the state-of-the-art method Scattered Forest Search (SFS) has been proposed, employing Monte Carlo Tree Search (MCTS) with carefully crafted initial seeds and text-based optimization. However, since SFS integrates multiple components, the effects of each component on performance and the overall behavior of SFS have not been sufficiently analyzed. In this work, we theoretically analyze the refinement process of SFS from the perspective of version spaces in learning theory and clarify its behavior. First, as a basis for the theoretical analysis, we introduce a sequential self-refinement method (Line), which starts from an initial program and repeatedly refines the resulting program. Furthermore, while Line progresses the refinement process in the depth direction, we introduce Iterative Refinement of Repair Instructions (IRRI) to capture the refinement process in the width direction, which fixes initial programs and iteratively refines repair instructions. We then analyze Line and IRRI and conduct a theoretical analysis of SFS by positioning it as an intermediate method between the two. Our theoretical analysis reveals that SFS exhibits behavior closer to IRRI than to Line, and this theoretical characteristic is also confirmed experimentally. Considering computational resources and methodological simplicity, these results suggest that a simpler method, IRRI, may achieve a similar refinement process without relying on the complex correction mechanism of SFS.

cs.LG↗

arXiv2026

SynIL: Leveraging Synergy for Offline Imitation Learning from Imperfect Demonstration Datasets

Imitation learning enables robots to acquire complex skills directly from massive demonstration datasets, but its performance degrades severely when datasets are contaminated with suboptimal or noisy demonstrations. While prior quality-assessment methods attempt to filter or reweight data, they typically rely on manual pre-selection of expert reference data or task-specific heuristics, limiting scalability. To address this challenge, we introduce SynIL (Synergy-based Imitation Learning), a novel framework for automated, label-free demonstration quality assessment in offline reinforcement learning. Grounded in neuroscientific evidence that motor synergy, a low-dimensional coordinated structure in movement, correlates directly with motor proficiency, SynIL algorithmically quantifies synergy manifestation to generate dense, transition-level reward signals via self-supervised reward regression. Comprehensive evaluations on D4RL locomotion benchmarks and multi-human Robomimic manipulation datasets demonstrate that synergy-derived rewards correlate strongly with ground-truth rewards. Furthermore, SynIL substantially outperforms Behavior Cloning (BC) and achieves performance comparable to, and in sparse-reward human teleoperation scenarios, superior to, offline reinforcement learning trained on true environment rewards.

cs.RO↗

arXiv2026

Dynamic Modeling and Parameter Estimation for Origami Structure Reconfiguration Process

The reconfiguration of origami during the folding and unfolding process is governed through a sequence of panel deformations and hinge orientations. To develop an effective model for representing the reconfiguration process, this paper introduces planar straight-line graphs and a novel consensus protocol for reaching the target origami configuration. The convergence and stability properties of the proposed consensus protocol are subsequently analyzed. Furthermore, to account for aggregate material and structural effects in the proposed consensus-based reconfiguration model, effective parameters embedded in the consensus protocol are identified from trajectory data using a fitting algorithm. Lastly, the effectiveness of the proposed modeling approach is shown using simulations of the two-panel structure and the Kresling origami pattern reconfiguration process.

math.DS↗

arXiv2023

Femtosecond reduction of atomic scattering factors triggered by intense x-ray pulse

X-ray diffraction of silicon irradiated with tightly focused femtosecond x-ray pulses (photon energy: 11.5 keV, pulse duration: 6 fs) was measured at various x-ray intensities up to $4.6\times10^{19}$ W/cm$^2$. The measurement reveals that the diffraction intensity is highly suppressed when the x-ray intensity reaches of the order of $10^{19}$ W/cm$^2$. With a dedicated simulation, we confirm the observed reduction of the diffraction intensity is attributed to the femtosecond change in individual atomic scattering factors due to the ultrafast creation of highly ionized atoms through photoionization, Auger decay, and subsequent collisional ionization. We anticipate that this ultrafast reduction of atomic scattering factor will be a basis for new x-ray nonlinear techniques, such as pulse shortening and contrast variation x-ray scattering.

physics.atom-ph↗