Whole-arm Grasping Strategy for Soft Arms to Capture Space Debris

Agabiti, Camilla; Ménager, Etienne; Falotico, Egidio

doi:10.1109/robosoft55895.2023.10122076

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…They conducted experiments on a soft robotic arm, preliminarily confirming the feasibility of using deep reinforcement learning for controlling soft robotic arms. After completing the first step of closed-loop control, Agabiti, Camilla A et al [52] devised a grasping strategy based on identifying contact points on objects to force the arm to bend and induce wrapping around the object. They verified in a simulated environment that a reinforcement learning strategy fused with an finite element simulation can induce deformation of the soft robotic arm to wrap around objects and perform grasping.…”

Section: Control Strategiesmentioning

confidence: 99%

Exploring Embodied Intelligence in Soft Robotics: A Review

Zhao,

Wu,

Wang

et al. 2024

Biomimetics

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Soft robotics is closely related to embodied intelligence in the joint exploration of the means to achieve more natural and effective robotic behaviors via physical forms and intelligent interactions. Embodied intelligence emphasizes that intelligence is affected by the synergy of the brain, body, and environment, focusing on the interaction between agents and the environment. Under this framework, the design and control strategies of soft robotics depend on their physical forms and material properties, as well as algorithms and data processing, which enable them to interact with the environment in a natural and adaptable manner. At present, embodied intelligence has comprehensively integrated related research results on the evolution, learning, perception, decision making in the field of intelligent algorithms, as well as on the behaviors and controls in the field of robotics. From this perspective, the relevant branches of the embodied intelligence in the context of soft robotics were studied, covering the computation of embodied morphology; the evolution of embodied AI; and the perception, control, and decision making of soft robotics. Moreover, on this basis, important research progress was summarized, and related scientific problems were discussed. This study can provide a reference for the research of embodied intelligence in the context of soft robotics.

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Section: Control Strategiesmentioning

confidence: 99%

Exploring Embodied Intelligence in Soft Robotics: A Review

Zhao,

Wu,

Wang

et al. 2024

Biomimetics

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show abstract

“…Jinglin Li proposed algorithms for autonomous whole-arm grasping operations, which include determining grasping configurations and progressively grasping to generate force-closure grasps in open and cluttered environments [40][41][42]. Camilla Agabiti introduced a whole-arm grasping strategy inspired by the elephant trunk, utilizing the contact points identified on the object to control the deformation of the soft arm [43]. However, the studies mentioned above do not consider the influence of the environment in the grasping process.…”

Section: Introductionmentioning

confidence: 99%

A Pre-Grasping Motion Planning Method Based on Improved Artificial Potential Field for Continuum Robots

Wang,

Sun,

Wang

et al. 2023

Sensors

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Secure and reliable active debris removal methods are crucial for maintaining the stability of the space environment. Continuum robots, with their hyper-redundant degrees of freedom, offer the ability to capture targets of varying sizes and shapes through whole-arm grasping, making them well-suited for active debris removal missions. This paper proposes a pre-grasping motion planning method for continuum robots based on an improved artificial potential field to restrict the movement area of the grasping target and prevent its escape during the pre-grasping phase. The analysis of the grasping workspace ensures that the target is within the workspace when starting the pre-grasping motion planning by dividing the continuum robot into delivery and grasping segments. An improved artificial potential field is proposed to guide the continuum robot in surrounding the target and creating a grasping area. Specifically, the improved artificial potential field consists of a spatial rotating potential field, an attractive potential field incorporating position and posture potential fields, and a repulsive potential field. The simulation results demonstrate the effectiveness of the proposed method. A comparison of motion planning results between methods that disregard and consider the posture potential field shows that the inclusion of the posture potential field improves the performance of pre-grasping motion planning for spatial targets, achieving a success rate of up to 97.8%.

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Pushing with Soft Robotic Arms via Deep Reinforcement Learning

Alessi,

Bianchi,

Stano

et al. 2024

Advanced Intelligent Systems

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Soft robots can adaptively interact with unstructured environments. However, nonlinear soft material properties challenge modeling and control. Learning‐based controllers that leverage efficient mechanical models are promising for solving complex interaction tasks. This article develops a closed‐loop pose/force controller for a dexterous soft manipulator enabling dynamic pushing tasks using deep reinforcement learning. Force tests investigate the mechanical properties of a soft robot module, resulting in orthogonal forces of N. Then, the policy is trained in simulation leveraging a dynamic Cosserat rod model of the soft robot. Domain randomization mitigate the sim‐to‐real gap while careful reward engineering induced pose and force control even without explicit force inputs. Despite the approximate simulation, the sim‐to‐real transfer achieved an average reaching distance of mm (), an average orientation error of rad () and applied pushing forces up to N. Such performance is reasonable for the intended assistive tasks of the manipulator. The experiments uncovered that the soft robot interacting with the environment exhibited torsional and counter‐balancing movements. Although not explicitly enforced, they emerged from the mechanical intelligence of the manipulator. The results demonstrate the potential of soft robotic manipulation via reinforcement learning.

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Whole-arm Grasping Strategy for Soft Arms to Capture Space Debris

Cited by 4 publications

References 22 publications

Exploring Embodied Intelligence in Soft Robotics: A Review

Exploring Embodied Intelligence in Soft Robotics: A Review

A Pre-Grasping Motion Planning Method Based on Improved Artificial Potential Field for Continuum Robots

Pushing with Soft Robotic Arms via Deep Reinforcement Learning

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