Nonprehensile Dynamic Manipulation: A Survey

Ruggiero, Fabio; Lippiello, Vincenzo; Siciliano, Bruno

doi:10.1109/lra.2018.2801939

Cited by 130 publications

(67 citation statements)

References 97 publications

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“…It is not necessary to establish an accurate model of the controlled object, so it is very suitable for local path planning of basketball robots. 19 Fuzzy control theory combines traditional control algorithms with fuzzy mathematics to study inexact objects by simulating human thinking processes. 20 The basis of fuzzy control systems is the fuzzy set theory and fuzzy logic control.…”

Section: The Complements Of a And B Arementioning

confidence: 99%

RETRACTED: Design of basketball robot based on behavior-based fuzzy control

Zhi

Jiang

2020

International Journal of Advanced Robotic Systems

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Aiming at the strong dependence on environmental information in traditional algorithms, the path planning of basketball robots in an unknown environment, and improving the safety of autonomous navigation, this article proposes a path planning algorithm based on behavior-based module control. In this article, fuzzy control theory is applied to the behavior control structure, and these two path planning algorithms are combined to solve the path planning problem of basketball robots in an unknown environment. First, the data of each sensor of the basketball robot configuration are simply fused. Then, the obstacle distance parameters in the three directions of front, left, and right are simplified and fuzzified. Then combined with the target direction parameters, the speed, and steering of the basketball robot are controlled by fuzzy rule reasoning to realize path planning. The simulation results show that the basketball robot can overcome the uncertainty in the environment, effectively achieve good path planning, verify the feasibility of the fuzzy control algorithm, and demonstrate the validity and correctness of the path planning strategy.

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Section: The Complements Of a And B Arementioning

confidence: 99%

RETRACTED: Design of basketball robot based on behavior-based fuzzy control

Zhi

Jiang

2020

International Journal of Advanced Robotic Systems

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“…The majority focus on explicit physics-based modeling of deformable behavior in simulation and they attempt to find an optimal trajectory to guarantee the desired outcome. Some studies have tried dynamic manipulations of flexible objects ( [8], [9], [10]) such as (dynamic) cloth folding, rope knotting, etc. with *This work has been supported by the ERC project Clothilde (ERC-2016-ADG-741930), the HuMoUR project TIN2017-90086-R (AEI/FEDER, UE) and by the AEI through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656).…”

Section: Introductionmentioning

confidence: 99%

Dynamic Cloth Manipulation with Deep Reinforcement Learning

Jangir

Alenyà

Torras

2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

101

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In this paper we present a Deep Reinforcement Learning approach to solve dynamic cloth manipulation tasks. Differing from the case of rigid objects, we stress that the followed trajectory (including speed and acceleration) has a decisive influence on the final state of cloth, which can greatly vary even if the positions reached by the grasped points are the same. We explore how goal positions for non-grasped points can be attained through learning adequate trajectories for the grasped points. Our approach uses few demonstrations to improve control policy learning, and a sparse reward approach to avoid engineering complex reward functions. Since perception of textiles is challenging, we also study different state representations to assess the minimum observation space required for learning to succeed. Finally, we compare different combinations of control policy encodings, demonstrations, and sparse reward learning techniques, and show that our proposed approach can learn dynamic cloth manipulation in an efficient way, i.e., using a reduced observation space, a few demonstrations, and a sparse reward.

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“…Related to our work is the survey conducted by Ruggiero et al ( 2018 ) which covers the literature on planning and control for non-prehensile dynamic manipulation. Pushing is one of the motion primitives which they consider, among throwing, catching, and others.…”

Section: Introductionmentioning

confidence: 99%

Let's Push Things Forward: A Survey on Robot Pushing

2020

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As robot make their way out of factories into human environments, outer space, and beyond, they require the skill to manipulate their environment in multifarious, unforeseeable circumstances. With this regard, pushing is an essential motion primitive that dramatically extends a robot's manipulation repertoire. In this work, we review the robotic pushing literature. While focusing on work concerned with predicting the motion of pushed objects, we also cover relevant applications of pushing for planning and control. Beginning with analytical approaches, under which we also subsume physics engines, we then proceed to discuss work on learning models from data. In doing so, we dedicate a separate section to deep learning approaches which have seen a recent upsurge in the literature. Concluding remarks and further research perspectives are given at the end of the paper. Stüber et al. Let's Push Things Forward PROBLEM STATEMENTEven in ideal conditions, such as structured environments where an agent has a complete model of the environment and perfect sensing abilities, the problems of robotic grasping and manipulation are not trivial. By complete model of the environment we mean that physical and geometric properties of the world, such as pose, shape, friction parameters and the mass of the object we wish to manipulate, are exactly known. In fact, the object to be manipulated is indirectly controlled by contacts with a robot manipulator (e.g. pushing by a contacting finger part), and an inverse model (IM), which computes an action to produce the desired motion or set of forces on the object, may not be known. Sometimes forward models (FM) may be fully or partially known, even where IMs are not available. In such cases, an FM can be used to estimate the next state of a system, given the current state and a set of executable actions. This enables planning to be 4. Physics engines. It employs a physics engine as a "black box" to make predictions about the interactions.5. Data-driven. It learns how to predict physical interaction from examples. 6. Deep learning. As the data-driven approaches, it learns how to construct an FM from examples. The key insight is that the deep learning approaches are based on feature extraction.The features highlighted for each approach are as follows.• The assumptions made by the authors on their approach. We highlight i) the quasi-static assumption in the model, ii) if it is a seminal work on 2D shapes, and iii) if the method required a known model of the object to be manipulated.• The type of motion analysed in the paper, such as 1D, planar (2D translation and 1D rotation around the x−axis), or full 3D (3D translation and 3D rotation).• The aim of the paper. We distinguish between predicting the motion of the object, estimating physical parameters, planning pushes, and analysing a push to reach a stable grasp.

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Nonprehensile Dynamic Manipulation: A Survey

Cited by 130 publications

References 97 publications

RETRACTED: Design of basketball robot based on behavior-based fuzzy control

RETRACTED: Design of basketball robot based on behavior-based fuzzy control

Dynamic Cloth Manipulation with Deep Reinforcement Learning

Let's Push Things Forward: A Survey on Robot Pushing

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