arXiv subjects

Kohei Matsumoto

Publications and source records attributed to Kohei Matsumoto.

6 recordsLinked to original sources

arXiv2026

Performance-Preserving Online Adaptation in Social Navigation via Diffusion Steering

In social navigation, modeling the complex interactions between humans and robots is difficult, and deep reinforcement learning has therefore been actively studied. However, because simulation alone cannot fully reproduce diverse scenarios, robot dynamics, and the social conventions that vary across deployment environments, fine-tuning in the deployment environment is promising. In doing so, learning that preserves the base model's performance is required, so as not to compromise the primary objective of navigation, namely avoiding pedestrians and reaching the destination. In this study, we propose a method that applies diffusion steering via reinforcement learning (DSRL), which trains only the noise policy while keeping the diffusion policy fixed, thereby achieving learning that preserves performance. Furthermore, we integrate diffusion-based RL policies trained with multiple seeds to construct the base policy, improving learning performance. Our evaluation shows that, compared with other methods, the proposed method enables efficient learning while preserving performance, and we confirm flexible behavior control through adaptation to social conventions, as well as its effectiveness on a physical robot through hardware-in-the-loop simulation.

cs.RO↗

arXiv2026

Incremental Residual Reinforcement Learning Toward Real-World Learning for Social Navigation

As the demand for mobile robots continues to increase, social navigation has emerged as a critical task, driving active research into deep reinforcement learning (RL) approaches. However, because pedestrian dynamics and social conventions vary widely across different regions, simulations cannot easily encompass all possible real-world scenarios. Real-world RL, in which agents learn while operating directly in physical environments, presents a promising solution to this issue. Nevertheless, this approach faces significant challenges, particularly regarding constrained computational resources on edge devices and learning efficiency. In this study, we propose incremental residual RL (IRRL). This method integrates incremental learning, which is a lightweight process that operates without a replay buffer or batch updates, with residual RL, which enhances learning efficiency by training only on the residuals relative to a base policy. Through the simulation experiments, we demonstrated that, despite lacking a replay buffer, IRRL achieved performance comparable to those of conventional replay buffer-based methods and outperformed existing incremental learning approaches. Furthermore, the real-world experiments confirmed that IRRL can enable robots to effectively adapt to previously unseen environments through the real-world learning.

cs.RO↗

arXiv2026

COLSON: Controllable Learning-Based Social Navigation via Diffusion-Based Reinforcement Learning

Mobile robot navigation in dynamic environments with pedestrian traffic is a key challenge in the development of autonomous mobile service robots. Recently, deep reinforcement learning-based methods have been actively studied and have outperformed traditional rule-based approaches owing to their optimization capabilities. Among these methods, those that assume continuous action spaces typically rely on Gaussian distributions, which limit the flexibility of the generated actions. In contrast, the application of diffusion models to reinforcement learning has advanced, enabling more flexible action distributions than Gaussian policy-based approaches. In this study, we apply a diffusion-based reinforcement learning approach to social navigation and validate its effectiveness. Furthermore, by exploiting the characteristics of diffusion models, we propose extensions that enable adaptation to previously unseen scenarios without additional training. As concrete scenario examples, we demonstrate adaptability to scenarios in which static obstacles exist in the environment that were not present during training, as well as scenarios in which the objective differs from training, such as accompanying target pedestrians while avoiding others to reach the destination.

cs.RO↗

arXiv2016

Holographic QCD for H-dibaryon (uuddss)

The H-dibaryon (uuddss) is studied in holographic QCD for the first time. In holographic QCD, four-dimensional QCD, i.e., SU($N_c$) gauge theory with chiral quarks, can be formulated with $S^1$-compactified D4/D8/$\overline{\rm D8}$-brane system. In holographic QCD with large $N_c$, all the baryons appear as topological chiral solitons of Nambu-Goldstone bosons and (axial) vector mesons, and the H-dibaryon can be described as an SO(3)-type topological soliton with $B=2$. We derive the low-energy effective theory to describe the H-dibaryon in holographic QCD. The H-dibaryon mass is found to be twice of the $B=1$ hedgehog-baryon mass, $M_{\rm H} \simeq 2.00 M_{B=1}^{\rm HH}$, and is estimated about 1.7GeV, which is smaller than mass of two nucleons (flavor-octet baryons), in the chiral limit.

hep-th↗

arXiv2016

A Study of the H-dibaryon in Holographic QCD

We study the H-dibaryon (uuddss) in holographic QCD for the first time. Holographic QCD is derived from a QCD-equivalent D-brane system ($S^1$-compactified D4/D8/$\overline{\rm D8}$) in the superstring theory via the gauge/gravity correspondence. In holographic QCD, all baryons appear as topological chiral solitons of Nambu-Goldstone bosons and (axial) vector mesons. In this framework, the H-dibaryon can be described as an SO(3)-type hedgehog state. We present the formalism of the H-dibaryon in holographic QCD, and perform the calculation to investigate its properties in the chiral limit.

hep-th↗

arXiv2016

1+1 Large $N_c$ QCD and its Holographic Dual -Soliton Picture of Baryons in Single-Flavor World

We study baryons in holographic QCD corresponding to 1+1 dimensional single-flavor ($N_f$=1) QCD for the first time. We formulate 1+1 QCD using an $S^1$-compactified D2/D8/$\overline{\rm D8}$ branes in the superstring theory, and describe the baryon as a topological configuration in 1+1 $N_f$=1 QCD, corresponding to $Π_1({\rm U(1)})={\bf Z}$. Unlike 1+3 QCD with $N_f \ge 2$, however, we find that the low-dimensional baryonic soliton is generally unstable against a scale transformation/variation and swells infinitely in 1+1 $N_f$=1 QCD at the leading of large $N_c$. We thus point out a serious difficulty on the soliton picture of baryons in large $N_c$ in the single-flavor world in both 1+1 and 1+3 QCD. We also compare the low-dimensional holographic baryon with the Abrikosov vortex, i.e., a stable topological configuration in Type-II superconductors.

hep-th↗