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Toshisada Mariyama

9 accepted papers

2023

A Soft, Multi-Layer, Kirigami Inspired Robotic Gripper with a Compact, Compression-Based Actuation System

Joao Buzzatto, Junbang Liang, Mojtaba Shahmohammadi, Saori Matsunaga, Rintaro Haraguchi, Toshisada Mariyama +2
IROS 2023poster

Over the last decade, a plethora of soft robotic devices have been proposed for the execution of complex grasping and dexterous manipulation tasks. Tasks requiring such increased dexterity are typically executed using fully-actuated, rigid end-effectors equipped with sophisticated sensing and contro…

Cited by 3Source ↗Scholar ↗
2023

Employing Multi-Layer, Sensorised Kirigami Grippers for Single-Grasp Based Identification of Objects and Force Exertion Estimation

Junbang Liang, Joao Buzzatto, Bryan Busby, Ricardo V. Godoy, Saori Matsunaga, Rintaro Haraguchi +3
IROS 2023poster

Soft robotic devices have been popular in handling intricate grasping and dexterous manipulation tasks, serving as an alternative to conventional, rigid end-effectors. These devices are relatively simple, lightweight, and cost-effective. Recently, kirigami based structures have been used to create l…

Cited by 1Source ↗Scholar ↗
2022

On Robotic Manipulation of Flexible Flat Cables: Employing a Multi-Modal Gripper with Dexterous Tips, Active Nails, and a Reconfigurable Suction Cup Module

Joao Buzzatto, Jayden Chapman, Mojtaba Shahmohammadi, Felipe Sanches, Mahla Nejati, Saori Matsunaga +4
IROS 2022poster

A popular solution for connecting different components in modern electronics, such as mobile phones, laptops, tablets, etc, is the use of flexible flat cables (FFC). Typically, it takes hours of repetition from a highly trained worker, or a high precision autonomous robot with specialised end effect…

Cited by 4Source ↗Scholar ↗
2022

Soft, Multi-Layer, Disposable, Kirigami Based Robotic Grippers: On Handling of Delicate, Contaminated, and Everyday Objects

Joao Buzzatto, Mojtaba Shahmohammadi, Junbang Liang, Felipe Sanches, Saori Matsunaga, Rintaro Haraguchi +3
IROS 2022poster

Grasping and manipulation are complex and demanding tasks, especially when executed in dynamic and unstructured environments. Typically, such tasks are executed by rigid articulated end-effectors, with a plethora of actuators that need sophisticated sensing and complex control laws to execute them e…

Cited by 8Source ↗Scholar ↗
2021

A Dexterous, Reconfigurable, Adaptive Robot Hand Combining Anthropomorphic and Interdigitated Configurations

Geng Gao, Jayden Chapman, Saori Matsunaga, Toshisada Mariyama, Bruce MacDonald, Minas Liarokapis
IROS 2021poster

Robot grasping and dexterous, in-hand manipulation allow robots to interact with their surroundings and execute a plethora of complex tasks such as pushing buttons, opening doors, and interacting with electrical appliances. In robotics, such complicated tasks are typically executed by multi-fingered…

Cited by 13Source ↗Scholar ↗
2021

A Locally-Adaptive, Parallel-Jaw Gripper with Clamping and Rolling Capable, Soft Fingertips for Fine Manipulation of Flexible Flat Cables

Jayden Chapman, Gal Gorjup, Anany Dwivedi, Saori Matsunaga, Toshisada Mariyama, Bruce MacDonald +1
ICRA 2021poster

Flexible flat cables (FFC) are very popular for connecting different components in modern electronics (e.g., mobile phones, laptops, tablets, etc.). The manipulation of FFCs typically relies on highly trained workers that spend hours performing the same repetitive processes, or on autonomous robotic…

Cited by 21Source ↗Scholar ↗
2020

Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?

Kei Ota, Tomoaki Oiki, Devesh Jha, Toshisada Mariyama, Daniel Nikovski
ICML 2020poster

Deep reinforcement learning (RL) algorithms have recently achieved remarkable successes in various sequential decision making tasks, leveraging advances in methods for training large deep networks. However, these methods usually require large amounts of training data, which is often a big problem fo…

Cited by 68Source ↗PDF ↗Scholar ↗
2020

Deep Reactive Planning in Dynamic Environments

Kei Ota, Devesh Jha, Tadashi Onishi, Asako Kanezaki, Yusuke Yoshiyasu, Yoko Sasaki +2
CoRL 2020

The main novelty of the proposed approach is that it allows a robot to learn an end-to-end policy which can adapt to changes in the environment during execution. While goal conditioning of policies has been studied in the RL literature, such approaches are not easily extended to settings where the r

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2019

Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning

Kei Ota, Devesh K. Jha, Tomoaki Oiki, Mamoru Miura, Takashi Nammoto, Daniel Nikovski +1
IROS 2019poster

In this paper, we propose a reinforcement learning-based algorithm for trajectory optimization for constrained dynamical systems. This problem is motivated by the fact that for most robotic systems, the dynamics may not always be known. Generating smooth, dynamically feasible trajectories could be d…

Cited by 42Source ↗Scholar ↗