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Mai Nishimura

11 accepted papers

2026

Tactile Memory With Soft Robot: Robust Object Insertion via Masked Encoding and Soft Wrist

Tatsuya Kamijo, Mai Nishimura, Nodoka Shibasaki, Jeremy Siburian, Cristian C. Beltran-Hernandez, Masashi Hamaya
RA-L 2026

Tactile memory, the ability to store and retrieve touch-based experience, is critical for contact-rich tasks such as key insertion under uncertainty. To replicate this capability, we introduce Tactile Memory with Soft Robot (TaMeSo-bot), a system that integrates a soft wrist with tactile retrieval-b

Cited by 0Source ↗Scholar ↗
2024

Multi-Agent Behavior Retrieval: Retrieval-Augmented Policy Training for Cooperative Push Manipulation by Mobile Robots

So Kuroki, Mai Nishimura, Tadashi Kozuno
IROS 2024poster

Due to the complex interactions between agents, learning multi-agent control policy often requires a prohibitive amount of data. This paper aims to enable multi-agent systems to effectively utilize past memories to adapt to novel collaborative tasks in a data-efficient fashion. We propose the Multi-…

Cited by 1Source ↗Scholar ↗
2024

Robot Swarm Control Based on Smoothed Particle Hydrodynamics for Obstacle-Unaware Navigation

Michikuni Eguchi, Mai Nishimura, Shigeo Yoshida, Takefumi Hiraki
IROS 2024poster

Robot swarms hold immense potential for performing complex tasks far beyond the capabilities of individual robots. However, the challenge in unleashing this potential is the robots’ limited sensory capabilities, which hinder their ability to detect and adapt to unknown obstacles in real-time. To ove…

Cited by 0Source ↗Scholar ↗
2024

When to Replan? An Adaptive Replanning Strategy for Autonomous Navigation using Deep Reinforcement Learning

Kohei Honda, Ryo Yonetani, Mai Nishimura, Tadashi Kozuno
ICRA 2024poster

The hierarchy of global and local planners is one of the most commonly utilized system designs in autonomous robot navigation. While the global planner generates a reference path from the current to goal locations based on the pre-built map, the local planner produces a kinodynamic trajectory to fol…

Cited by 4Source ↗Scholar ↗
2023

ViewBirdiformer: Learning to Recover Ground-Plane Crowd Trajectories and Ego-Motion From a Single Ego-Centric View

Mai Nishimura, Shohei Nobuhara, Ko Nishino
RA-L 2023

We introduce a novel learning-based method for view birdification [1], the task of recovering ground-plane trajectories of pedestrians of a crowd and their observer in the same crowd just from the observed ego-centric video. View birdification becomes essential for mobile robot navigation and locali

Cited by 4Source ↗Scholar ↗
2022

Prioritized Safe Interval Path Planning for Multi-Agent Pathfinding With Continuous Time on 2D Roadmaps

Kazumi Kasaura, Mai Nishimura, Ryo Yonetani
RA-L 2022

We address a challenging multi-agent pathfinding (MAPF) problem for hundreds of agents moving on a 2D roadmap with continuous time. Despite its known potential for producing better solutions compared to typical grid and discrete-time cases, few approaches have been established to solve this problem

Cited by 29Source ↗Scholar ↗
2021

Path Planning using Neural A* Search

Ryo Yonetani, Tatsunori Taniai, Mohammadamin Barekatain, Mai Nishimura, Asako Kanezaki
ICML 2021spotlight

We present Neural A*, a novel data-driven search method for path planning problems. Despite the recent increasing attention to data-driven path planning, machine learning approaches to search-based planning are still challenging due to the discrete nature of search algorithms. In this work, we refor…

Cited by 129Source ↗PDF ↗code ↗Scholar ↗
2020

L2B: Learning to Balance the Safety-Efficiency Trade-off in Interactive Crowd-aware Robot Navigation

Mai Nishimura, Ryo Yonetani
IROS 2020poster

This work presents a deep reinforcement learning framework for interactive navigation in a crowded place. Our proposed Learning to Balance (L2B) framework enables mobile robot agents to steer safely towards their destinations by avoiding collisions with a crowd, while actively clearing a path by ask…

Cited by 42Source ↗Scholar ↗
2015

A Linear Generalized Camera Calibration From Three Intersecting Reference Planes

Mai Nishimura, Shohei Nobuhara, Takashi Matsuyama, Shinya Shimizu, Kensaku Fujii
ICCV 2015poster

This paper presents a new generalized (or ray-pixel, raxel) camera calibration algorithm for camera systems involving distortions by unknown refraction and reflection processes. The key idea is use of intersections of calibration planes, while conventional methods utilized collinearity constraints o…

Cited by 13PDF ↗Scholar ↗