The spatiotemporal structure of control variables during catching
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The paper discusses the scale-dependent grasp. Suppose that a human approaches an object initially placed on a table and finally achieves an enveloping grasp. Under such initial and final conditions, he (or she) unconsciously changes the grasp strategy according to the size of objects, even though they have similar geometry. We call the grasp planning the scale-dependent grasp. We find that grasp patterns are also changed according to the surface friction and the geometry of cross section in addition to the scale of object. Focusing on column objects, we first classify the grasp patterns and extract the essential motions so that we can construct grasp strategies applicable to multifingered robot hands. The grasp strategies constructed for robot hands are verified by experiments. We also consider how a robot hand can recognize the failure mode and how it can switch from one to another.
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This paper deals with the problem of planning hand configuration for grasp/non-grasp constraining of an object. In order to make a robot perform grasp and graspless manipulation by a multifingered hand, we need a planner to determine hand configuration applicable to both of them. We implement a new performance index on a grasp planner, GraspIt!, so that we can obtain appropriate hand configuration for grasp/non-grasp constraining of an object.
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The way we grasp an object depends on several factors, e.g. the intended goal or the hand's anatomy. Therefore, a grasp can convey meaningful information about its context. Inferring these factors from a grasp allows us to enhance interaction with grasp-sensitive objects. This paper highlights an grasp as an important source of meaningful context for human-computer interaction and gives an overview of prior work from other disciplines. This paper offers a basis and framework for further research and discussion by proposing a descriptive model of meaning in grasps. The GRASP model combines five factors that determine how an object is grasped: goal, relationship between user and object, anatomy, setting, and properties of the object. The model is validated both from an epistemological perspective and by applying it to scenarios from related work.
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We introduce AO-Grasp, a grasp proposal method that generates 6 DoF grasps that enable robots to interact with articulated objects, such as opening and closing cabinets and appliances. AO-Grasp consists of two main contributions: the AO-Grasp Model and the AO-Grasp Dataset. Given a segmented partial point cloud of a single articulated object, the AO-Grasp Model predicts the best grasp points on the object with an Actionable Grasp Point Predictor. Then, it finds corresponding grasp orientations for each of these points, resulting in stable and actionable grasp proposals. We train the AO-Grasp Model on our new AO-Grasp Dataset, which contains 78K actionable parallel-jaw grasps on synthetic articulated objects. In simulation, AO-Grasp achieves a 45.0 % grasp success rate, whereas the highest performing baseline achieves a 35.0% success rate. Additionally, we evaluate AO-Grasp on 120 real-world scenes of objects with varied geometries, articulation axes, and joint states, where AO-Grasp produces successful grasps on 67.5% of scenes, while the baseline only produces successful grasps on 33.3% of scenes. To the best of our knowledge, AO-Grasp is the first method for generating 6 DoF grasps on articulated objects directly from partial point clouds without requiring part detection or hand-designed grasp heuristics. Project website: https://stanford-iprl-lab.github.io/ao-grasp
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We propose CAPGrasp, an $\mathbb{R}^3\times \text{SO(2)-equivariant}$ 6-DoF continuous approach-constrained generative grasp sampler. It includes a novel learning strategy for training CAPGrasp that eliminates the need to curate massive conditionally labeled datasets and a constrained grasp refinement technique that improves grasp poses while respecting the grasp approach directional constraints. The experimental results demonstrate that CAPGrasp is more than three times as sample efficient as unconstrained grasp samplers while achieving up to 38% grasp success rate improvement. CAPGrasp also achieves 4-10% higher grasp success rates than constrained but noncontinuous grasp samplers. Overall, CAPGrasp is a sample-efficient solution when grasps must originate from specific directions, such as grasping in confined spaces.
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For a multi-fingered hand to grasp an object, there are numerous ways to grasp it stably, and thus an optimal grasp planning is necessary to find the optimal grasp point for achieving the objective of the given task. First, we define several grasp indices to evaluate the quality of each feasible grasp. Since the physical meanings of the defined grasp induces are different from each other, it is not easy to combine those indices to identify the optimal grasping. In this paper, we propose a new generalized grasping performance index to represent all of the grasp indices as one measure based on a non-dimensional technique. Through simulations, we show that the proposed optimal grasp planning is resemblant to the physical sense of human grasping.
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This paper deals with an identification of contact points between finger links and a grasped object in an enveloping grasp. The grasp has the following merits. One merit is that the grasp is firmer than a fingertip grasp and a distal link grasp. Another merit is that error of contact points is recognized because the object is grasped by inner links. However, it is difficult to identify contact points and contact forces. These parameters are necessary to control the object, not to destroy it, and to shift to an optimum grasp. We propose an analytical method for an identification of unknown parameters, such as contact points and forces, by active sensing in the enveloping grasp. A necessary and sufficient condition for the identification is provided.
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