Simulation-Driven Evolutionary Motion Parameterization for Contact-Rich Granular Scooping with a Soft Conical Robotic Hand

• URL: http://arxiv.org/abs/2604.05531v1
• Date: Tue, 07 Apr 2026 07:30:21 GMT
• Title: Simulation-Driven Evolutionary Motion Parameterization for Contact-Rich Granular Scooping with a Soft Conical Robotic Hand
• Abstract summary: Tool-based scooping is vital in robot-assisted tasks, enabling interaction with objects of varying sizes, shapes, and material states.<n>Recent studies have shown that flexible, reconfigurable soft robotic end-effectors can adapt their shape to maintain consistent contact during scooping.<n>These soft tools can adjust to varying container sizes and materials without requiring complex sensing or control.<n>This paper presents the development of a physics-based simulation model of a deformable soft conical robotic hand.
• Score: 14.911239660367926
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Tool-based scooping is vital in robot-assisted tasks, enabling interaction with objects of varying sizes, shapes, and material states. Recent studies have shown that flexible, reconfigurable soft robotic end-effectors can adapt their shape to maintain consistent contact with container surfaces during scooping, improving efficiency compared to rigid tools. These soft tools can adjust to varying container sizes and materials without requiring complex sensing or control. However, the inherent compliance and complex deformation behavior of soft robotics introduce significant control complexity that limits practical applications. To address this challenge, this paper presents the development of a physics-based simulation model of a deformable soft conical robotic hand that captures its passive reconfiguration dynamics and enables systematic trajectory optimization for scooping tasks. We propose a novel physics-based simulation approach that accurately models the soft tool's morphing behavior from flat sheets to adaptive conical structures, combined with an evolutionary strategy framework that automatically optimizes scooping trajectories without manual parameter tuning. We validate the optimized trajectories through both simulation and real-robot experiments. The results demonstrate strong generalization and successfully address a range of challenging tasks previously beyond the reach of existing approaches. Videos of our experiments are available online: https://sites.google.com/view/scoopsh

Related papers

• Trajectory Generation for Underactuated Soft Robot Manipulators using Discrete Elastic Rod Dynamics [2.739506394240299]
  Planning motions with dynamically-feasible trajectories requires models that capture arbitrary deformations.<n>We present our trajectory generation framework and validate it experimentally on a pneumatic soft robotic limb.
  arXiv  Detail & Related papers  (2026-03-23T22:01:14Z)
• A Unified Low-Dimensional Design Embedding for Joint Optimization of Shape, Material, and Actuation in Soft Robots [4.829343342713712]
  We introduce a smooth, low-dimensional design embedding for soft robots that unifies shape morphing, multi-material distribution, and actuation.<n>In experiments, we show that design increases with the number of basis functions, unlike comparable neural network encodings.<n>We further show that joint co-optimization of shape, material, and actuation using our unified embedding consistently outperforms sequential strategies.
  arXiv  Detail & Related papers  (2026-03-06T17:33:32Z)
• D-REX: Differentiable Real-to-Sim-to-Real Engine for Learning Dexterous Grasping [66.22412592525369]
  We introduce a real-to-sim-to-real engine that leverages the Gaussian Splat representations to build a differentiable engine.<n>We show that our engine achieves accurate and robust performance in mass identification across various object geometries and mass values.<n>Those optimized mass values facilitate force-aware policy learning, achieving superior and high performance in object grasping.
  arXiv  Detail & Related papers  (2026-03-01T15:32:04Z)
• SORS: A Modular, High-Fidelity Simulator for Soft Robots [5.660024841218955]
  Soft robots pose unique modeling challenges due to their large nonlinear deformations, material incompressibility, and contact interactions.<n>We present SORS, a versatile, high-fidelity simulator designed to handle these complexities for soft robot applications.
  arXiv  Detail & Related papers  (2025-12-17T21:58:46Z)
• Surrogate compliance modeling enables reinforcement learned locomotion gaits for soft robots [5.193389420813379]
  Adaptive morphogenetic robots adapt their morphology and control policies to meet changing tasks and environmental conditions.<n>Many such systems leverage soft components, which enable shape morphing but also introduce simulation and control challenges.<n>Here, we present a surrogate compliance modeling approach: rather than explicitly modeling soft-body physics, we introduce indirect variables representing soft-material deformation within a rigid-body simulator.
  arXiv  Detail & Related papers  (2025-12-08T02:52:42Z)
• GausSim: Foreseeing Reality by Gaussian Simulator for Elastic Objects [55.02281855589641]
  GausSim is a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels.<n>We leverage continuum mechanics and treat each kernel as a Center of Mass System (CMS) that represents continuous piece of matter.<n>In addition, GausSim incorporates explicit physics constraints, such as mass and momentum conservation, ensuring interpretable results and robust, physically plausible simulations.
  arXiv  Detail & Related papers  (2024-12-23T18:58:17Z)
• Incremental Few-Shot Adaptation for Non-Prehensile Object Manipulation using Parallelizable Physics Simulators [5.483662156126757]
  We propose a novel approach for non-prehensile manipulation which incrementally adapts a physics-based dynamics model for model-predictive control (MPC)<n>We evaluate our few-shot adaptation approach in object pushing experiments in simulation and with a real robot.
  arXiv  Detail & Related papers  (2024-09-20T05:24:25Z)
• 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)
• Differentiable Simulation of Soft Multi-body Systems [99.4302215142673]
  We develop a top-down matrix assembly algorithm within Projective Dynamics. We derive a differentiable control framework for soft articulated bodies driven by muscles, joint torques, or pneumatic tubes.
  arXiv  Detail & Related papers  (2022-05-03T20:03:22Z)
• Gradient-Based Trajectory Optimization With Learned Dynamics [80.41791191022139]
  We use machine learning techniques to learn a differentiable dynamics model of the system from data. We show that a neural network can model highly nonlinear behaviors accurately for large time horizons. In our hardware experiments, we demonstrate that our learned model can represent complex dynamics for both the Spot and Radio-controlled (RC) car.
  arXiv  Detail & Related papers  (2022-04-09T22:07:34Z)
• Learning Material Parameters and Hydrodynamics of Soft Robotic Fish via Differentiable Simulation [26.09104786491426]
  Our framework allows high fidelity prediction of dynamic behavior for composite bi-morph bending structures in real hardware. We demonstrate an experimentally-verified, fast optimization pipeline for learning the material parameters and hydrodynamics of our robots. Although we focus on a specific application for underwater soft robots, our framework is applicable to any pneumatically actuated soft mechanism.
  arXiv  Detail & Related papers  (2021-09-30T05:24:02Z)
• PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics [89.81550748680245]
  We introduce a new differentiable physics benchmark called PasticineLab. In each task, the agent uses manipulators to deform the plasticine into the desired configuration. We evaluate several existing reinforcement learning (RL) methods and gradient-based methods on this benchmark.
  arXiv  Detail & Related papers  (2021-04-07T17:59:23Z)

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.