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This work represents an initial benchmark of a large-scale soft robot performing physical, collaborative manipulation of a long, extended object with a human partner. The robot consists of a pneumatically-actuated, three-link continuum soft…

Robotics · Computer Science 2025-04-14 Dallin L. Cordon , Shaden Moss , Marc Killpack , John L. Salmon

Model-based manipulation of deformable objects has traditionally dealt with objects while neglecting their dynamics, thus mostly focusing on very lightweight objects at steady state. At the same time, soft robotic research has made…

Robotics · Computer Science 2025-10-21 Sebastien Tiburzio , Tomás Coleman , Daniel Feliu-Talegon , Cosimo Della Santina

Musculoskeletal robots offer intrinsic compliance and flexibility, providing a promising paradigm for versatile locomotion. However, existing research typically relies on models with fixed muscle physiological parameters. This static…

Robotics · Computer Science 2026-04-15 Lidong Sun , Wentao Zhao , Ye Wang , Huaping Liu , Fuchun Sun

Physical interactive robotics, ranging from wearable devices to collaborative humanoid robots, require close coordination between mechanical design and control. However, evaluating interactive dynamics is challenging due to complex human…

Robotics · Computer Science 2026-03-11 Chenhui Zuo , Jinhao Xu , Michael Qian Vergnolle , Yanan Sui

Soft robots are known for their ability to perform tasks with great adaptability, enabled by their distributed, non-uniform stiffness and actuation. Bending is the most fundamental motion for soft robot design, but creating robust, and…

Soft Robots distinguish themselves from traditional robots by embracing flexible kinematics. Because of their recent emergence, there exist numerous uncharted territories, including novel actuators, manufacturing processes, and advanced…

Robotics · Computer Science 2024-01-23 Jorge Francisco García-Samartín , Adrián Rieker , Antonio Barrientos

Robotic performance emerges from the coupling of body and controller, yet it remains unclear when morphology-control co-design is necessary. We present a unified framework that embeds morphology and control parameters within a single neural…

Neural and Evolutionary Computing · Computer Science 2025-10-10 Yi Zhang , Yue Xie , Tao Sun , Fumiya Iida

Tailoring the design of robot bodies for control purposes is implicitly performed by engineers, however, a methodology or set of tools is largely absent and optimization of morphology (shape, material properties of robot bodies, etc.) is…

Robotics · Computer Science 2015-10-27 Matej Hoffmann , Vincent C. Müller

Co-designing a robot's morphology and control can ensure synergistic interactions between them, prevalent in biological organisms. However, co-design is a high-dimensional search problem. To make this search tractable, we need a systematic…

Robotics · Computer Science 2026-04-14 Apoorv Vaish , Oliver Brock

The vision of creating entirely-soft robots capable of performing complex tasks will be accomplished only when the controllers required for autonomous operation can be fully implemented on soft components. Despite recent advances in…

Robotics · Computer Science 2020-03-10 Ke Xu , Nestor O. Perez-Arancibia

High-performance closed-loop control of truly soft continuum manipulators has remained elusive. Experimental demonstrations have largely relied on sufficiently stiff, piecewise architectures in which each actuated segment behaves as a…

Robotics · Computer Science 2026-04-14 Vito Daniele Perfetta , Daniel Feliu-Talegon , Ebrahim Shahabi , Cosimo Della Santina

The Finite Element Method (FEM) is a powerful modeling tool for predicting soft robots' behavior, but its computation time can limit practical applications. In this paper, a learning-based approach based on condensation of the FEM model is…

While significant research progress has been made in robot learning for control, unique challenges arise when simultaneously co-optimizing morphology. Existing work has typically been tailored for particular environments or representations.…

We propose reaching steps towards the real-time strain control of multiphysics, multiscale continuum soft robots. To study this problem fundamentally, we ground ourselves in a model-based control setting enabled by mathematically precise…

Systems and Control · Electrical Eng. & Systems 2025-05-09 Lekan Molu

Soft robotics holds transformative potential for enabling adaptive and adaptable systems in dynamic environments. However, the interplay between morphological and control complexities and their collective impact on task performance remains…

Robotics · Computer Science 2025-03-27 Yue Xie , Kai-fung Chu , Xing Wang , Fumiya Iida

Active control of the shape of soft robots is challenging. Despite having an infinite number of passive degrees of freedom (DOFs), soft robots typically only have a few actively controllable DOFs, limited by the number of degrees of…

Robotics · Computer Science 2023-07-10 Brian H. Do , Shuai Wu , Ruike Renee Zhao , Allison M. Okamura

Soft robots offer more flexibility, compliance, and adaptability than traditional rigid robots. They are also typically lighter and cheaper to manufacture. However, their use in real-world applications is limited due to modeling challenges…

Natural organisms utilize distributed actuation through their musculoskeletal systems to adapt their gait for traversing diverse terrains or to morph their bodies for varied tasks. A longstanding challenge in robotics is to emulate this…

Traditional robots have rigid links and structures that limit their ability to interact with the dynamics of their immediate environment. For example, conventional robot manipulators with rigid links can only manipulate objects using…

Robotics · Computer Science 2019-10-09 Gideon Gbenga Oladipupo

Rigid-bodied robots often lack compliance needed to adapt to unstructured environments, while fully soft robots, though highly adaptable, struggle with scalability and load capacity. In nature, musculoskeletal systems balance strength and…

Computational Engineering, Finance, and Science · Computer Science 2026-05-29 Hiroki Kobayashi , Yuki Takaha , Changyoung Yuhn , Yuki Sato , Sunao Tomita , Atsushi Kawamoto , Tsuyoshi Nomura