Design Optimization for Large High-Force Soft Robot Manipulators Under Gravitational Loads

• URL: http://arxiv.org/abs/2608.16728v1
• Date: Mon, 17 Aug 2026 15:42:34 GMT
• Title: Design Optimization for Large High-Force Soft Robot Manipulators Under Gravitational Loads
• Abstract summary: This manuscript introduces a method for optimizing the geometry of a soft robot limb.<n>We demonstrate that an explicit solution exists to the proposed optimization problem under certain assumptions.<n> Experiments with three geometries of a large, soft, pneumatically-actuated manipulator demonstrate that the method correctly predicts which designs meet constraints.
• Score: 0.037475012732344194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Designing large soft robots capable of generating high forces for physical human-robot interaction remains a significant challenge in soft robotics. Prior work in large soft robots has focused on proof-of-concept prototypes, and no systematic framework exists for determining the suitability of a design paradigm for a desired task. This manuscript introduces a method for optimizing the geometry of a soft robot limb, maximizing its blocking force subject to an anti-bucking constraint under its own gravitational loading. We demonstrate that an explicit solution exists to the proposed optimization problem under certain assumptions. Experiments with three geometries of a large, soft, pneumatically-actuated manipulator demonstrate that the method correctly predicts which designs meet constraints and which produces the largest end-effector forces. This method, with its closed-form solution, can allow designers to determine a-priori if an intended class of soft manipulators is an appropriate choice for physical interaction at large size scales.

Related papers

• Fast Functionally Redundant Inverse Kinematics for Robotic Toolpath Optimisation in Manufacturing Tasks [53.04487603529418]
  Inverse kinematics algorithms can solve this in a fast, reactive framework.<n>We propose a novel algorithm to solve functionally redundant inverse kinematics for robotic manipulation.
  arXiv  Detail & Related papers  (2025-12-10T22:07:07Z)
• Toward Humanoid Brain-Body Co-design: Joint Optimization of Control and Morphology for Fall Recovery [55.951691393378354]
  We propose RoboCraft, a scalable humanoid co-design framework for fall recovery.<n>A shared policy pretrained across multiple designs is progressively finetuned on high-performing morphologies.<n>Experiments show that RoboCraft achieves an average performance gain of 44.55% on seven public humanoid robots.
  arXiv  Detail & Related papers  (2025-10-25T15:40:18Z)
• A Design Co-Pilot for Task-Tailored Manipulators [21.513483509321805]
  This work proposes an approach to automatically designing and optimizing robot morphologies tailored to a specific environment.<n>A fully differentiable framework realizes gradient-based fine-tuning of designed robots and inverse kinematics solutions.<n> Numerical experiments show that our approach finds robots that can navigate cluttered environments, manipulators that perform well across a specified workspace, and can be adapted to different hardware constraints.
  arXiv  Detail & Related papers  (2025-09-16T13:34:30Z)
• AI-Enhanced Automatic Design of Efficient Underwater Gliders [60.45821679800442]
  Building an automated design framework is challenging due to the complexities of representing glider shapes and the high computational costs associated with modeling complex solid-fluid interactions.<n>We introduce an AI-enhanced automated computational framework designed to overcome these limitations by enabling the creation of underwater robots with non-trivial hull shapes.<n>Our approach involves an algorithm that co-optimizes both shape and control signals, utilizing a reduced-order geometry representation and a differentiable neural-network-based fluid surrogate model.
  arXiv  Detail & Related papers  (2025-04-30T23:55:44Z)
• Efficient Design of Compliant Mechanisms Using Multi-Objective Optimization [50.24983453990065]
  We address the synthesis of a compliant cross-hinge mechanism capable of large angular strokes.<n>We formulate a multi-objective optimization problem based on kinetostatic performance measures.
  arXiv  Detail & Related papers  (2025-04-23T06:29:10Z)
• Design Optimizer for Soft Growing Robot Manipulators in Three-Dimensional Environments [0.7209792639354117]
  Soft growing robots are novel devices that mimic plant-like growth for navigation in cluttered or dangerous environments.<n>This work presents an approach for design optimization of soft growing robots.<n>It suggests the optimal size of the robot for solving a specific task.
  arXiv  Detail & Related papers  (2024-12-31T09:44:18Z)
• A 'MAP' to find high-performing soft robot designs: Traversing complex design spaces using MAP-elites and Topology Optimization [5.774729866385869]
  There are no widely adopted computational design tools that produce quality, manufacturable designs. In this work, we investigate a hierarchical design optimization methodology to generate diverse and high-performance soft robots. Our method provides a new framework to design parts in complex design domains, both soft and rigid.
  arXiv  Detail & Related papers  (2024-07-10T12:27:17Z)
• DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models [102.13968267347553]
  We present DiffuseBot, a physics-augmented diffusion model that generates soft robot morphologies capable of excelling in a wide spectrum of tasks. We showcase a range of simulated and fabricated robots along with their capabilities.
  arXiv  Detail & Related papers  (2023-11-28T18:58:48Z)
• Model Predictive Control for Fluid Human-to-Robot Handovers [50.72520769938633]
  Planning motions that take human comfort into account is not a part of the human-robot handover process. We propose to generate smooth motions via an efficient model-predictive control framework. We conduct human-to-robot handover experiments on a diverse set of objects with several users.
  arXiv  Detail & Related papers  (2022-03-31T23:08:20Z)
• An End-to-End Differentiable Framework for Contact-Aware Robot Design [37.715596272425316]
  We build an end-to-end differentiable framework for contact-aware robot design. A novel deformation-based parameterization allows for the design of articulated rigid robots with arbitrary, complex geometry. A differentiable rigid body simulator can handle contact-rich scenarios and computes analytical gradients for a full spectrum of kinematic and dynamic parameters.
  arXiv  Detail & Related papers  (2021-07-15T17:53:44Z)
• Scalable and Probabilistically Complete Planning for Robotic Spatial Extrusion [0.755972004983746]
  We present a rigorous formalization of robotic spatial extrusion planning. We show that, although these constraints often conflict with each other, a greedy backward state-space search guided by a stiffness-aware is able to successfully balance both constraints.
  arXiv  Detail & Related papers  (2020-02-06T17:05:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.