Casting manipulation of unknown string by robot arm

Tabata, Kenta; Seki, Hiroaki; Tsuji, Tokuo; Hiramitsu, Tatsuhiro

doi:10.1109/iros51168.2021.9635837

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…The authors have realized several motion objectives for strings with unknown properties by repeating motion generation, actual manipulation, and parameter estimation [22] [23] [24]. Specifically, our proposed method differs from previous research as it does not acquire training samples by manipulating the robot in the real world in advance, as in previous studies.…”

Section: Contribution Of This Papermentioning

confidence: 99%

Casting Manipulation With Unknown String via Motion Generation, Actual Manipulation, and Parameter Estimation

Tabata,

Miyagusuku,

Seki

et al. 2024

IEEE Access

Self Cite

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In this manuscript, we propose a motion strategy for manipulating strings with unknown properties. Our approach iteratively refines its motion generation based on parameters estimated from observed string behavior, without the need for real-time feedback. This strategy has been shown effective in achieving several motion objectives using uniform strings of similar lengths. In this research, we improve upon this strategy by addressing the challenges posed by varying string lengths and non-uniform strings. For this, we utilize a non-uniform string model and address various string properties to demonstrate the feasibility of our proposed motion strategy. Experiments conducted with different string types and lengths (between 300 to 610mm), including some with non-uniform mass distributions, demonstrate our method's effectiveness. Results show that our proposed method functions effectively with various kinds of strings, regardless of length and mass distribution, without requiring precise model parameters. Unique to this approach is its ability to adapt to various string characteristics through parameter estimation and motion generation, significantly reducing the need for real-world manipulation trials. Our findings illustrate the potential of our method for use in advanced robotic applications that require handling deformable objects.

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Section: Contribution Of This Papermentioning

confidence: 99%

Casting Manipulation With Unknown String via Motion Generation, Actual Manipulation, and Parameter Estimation

Tabata,

Miyagusuku,

Seki

et al. 2024

IEEE Access

Self Cite

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“…Trajectory optimization uses analytical models Yamakawa et al (2011) or numerical simulation Li et al (2015); Nah et al (2020); Yoshida et al (2015); Jin et al (2021); Tabata et al (2021) to generate mostly open-loop solutions for deformable object manipulation. Once a model has been designed, or automatically identified Tabata et al (2021), the optimization problem can be solved using methods such as direct collocation Jin et al (2021), single shooting Li et al (2015) or black-box optimization Nah et al (2020). These methods are generally capable of handling a wide range of goals and generalizing to many object instances, as long as a model is available for each new object.…”

Section: Related Workmentioning

confidence: 99%

Iterative residual policy: For goal-conditioned dynamic manipulation of deformable objects

Chi,

Burchfiel,

Cousineau

et al. 2023

The International Journal of Robotics Research

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This paper tackles the task of goal-conditioned dynamic manipulation of deformable objects. This task is highly challenging due to its complex dynamics (introduced by object deformation and high-speed action) and strict task requirements (defined by a precise goal specification). To address these challenges, we present Iterative Residual Policy (IRP), a general learning framework applicable to repeatable tasks with complex dynamics. IRP learns an implicit policy via delta dynamics—instead of modeling the entire dynamical system and inferring actions from that model, IRP learns delta dynamics that predict the effects of delta action on the previously observed trajectory. When combined with adaptive action sampling, the system can quickly optimize its actions online to reach a specified goal. We demonstrate the effectiveness of IRP on two tasks: whipping a rope to hit a target point and swinging a cloth to reach a target pose. Despite being trained only in simulation on a fixed robot setup, IRP is able to efficiently generalize to noisy real-world dynamics, new objects with unseen physical properties, and even different robot hardware embodiments, demonstrating its excellent generalization capability relative to alternative approaches.

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“…Trajectory optimization uses analytical models [30] or numerical simulation [13,20,31,11,27] to generate mostly open-loop solutions for deformable object manipulation. Once a model has been designed, or automatically identified [27], the optimization problem can be solved using methods such as direct collocation [11], single shooting [13] or black-box optimization [20]. These methods are generally capable of handling a wide range of goals and generalizing to many object instances, as long as a model is available for each new object.…”

Section: Related Workmentioning

confidence: 99%

Iterative Residual Policy for Goal-Conditioned Dynamic Manipulation of Deformable Objects

Cheng¹,

Burchfiel²,

Cousineau³

et al. 2022

Robotics: Science and Systems XVIII

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This paper tackles the task of goal-conditioned dynamic manipulation of deformable objects. This task is highly challenging due to its complex dynamics (introduced by object deformation and high-speed action) and strict task requirements (defined by a precise goal specification). To address these challenges, we present Iterative Residual Policy (IRP), a general learning framework applicable to repeatable tasks with complex dynamics. IRP learns an implicit policy via delta dynamicsinstead of modeling the entire dynamical system and inferring actions from that model, IRP learns delta dynamics that predict the effects of delta action on the previously-observed trajectory. When combined with adaptive action sampling, the system can quickly optimize its actions online to reach a specified goal. We demonstrate the effectiveness of IRP on two tasks: whipping a rope to hit a target point and swinging a cloth to reach a target pose. Despite being trained only in simulation on a fixed robot setup, IRP is able to efficiently generalize to noisy real-world dynamics, new objects with unseen physical properties, and even different robot hardware embodiments, demonstrating its excellent generalization capability relative to alternative approaches.

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Casting manipulation of unknown string by robot arm

Cited by 5 publications

References 12 publications

Casting Manipulation With Unknown String via Motion Generation, Actual Manipulation, and Parameter Estimation

Casting Manipulation With Unknown String via Motion Generation, Actual Manipulation, and Parameter Estimation

Iterative residual policy: For goal-conditioned dynamic manipulation of deformable objects

Iterative Residual Policy for Goal-Conditioned Dynamic Manipulation of Deformable Objects

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