Grasp planning based on strategy extracted from demonstration

Lin, Yun; Sun, Yu

doi:10.1109/iros.2014.6943193

Cited by 23 publications

(18 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The human hand is highly complex with extensive soft tissue and a skeletal structure that is often modeled with 26 degrees of freedom. Hence, previous work has focused on recording grasping activity in other forms like hand joint configuration by manual annotation [49,3], data gloves [20,29] or wired magnetic trackers [54,16] (which can interfere with natural grasping), or model-based hand pose estimation [50]. At a higher level, grasping has been observed through thirdperson [52,21,36] or first-person [21,6,46] videos, in which frames are annotated with the category of grasp according to a grasp taxonomy [12,23].…”

Section: Datasets Of Human Graspsmentioning

confidence: 99%

“…A large body of previous work [20,29,36,46,49,3,50,52,21,36,21,6,46] has recorded human grasps, with methods ranging from data gloves that measure joint configuration to manually arranged robotic hands. ContactDB differs significantly from these previous datasets by focusing primarily on the contact resulting from the rich interaction between hand and object.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging

Brahmbhatt

Ham

Kemp

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

164

102

View full text Add to dashboard Cite

Grasping and manipulating objects is an important human skill. Since hand-object contact is fundamental to grasping, capturing it can lead to important insights. However, observing contact through external sensors is challenging because of occlusion and the complexity of the human hand. We present ContactDB, a novel dataset of contact maps for household objects that captures the rich handobject contact that occurs during grasping, enabled by use of a thermal camera. Participants in our study grasped 3D printed objects with a post-grasp functional intent. Con-tactDB includes 3750 3D meshes of 50 household objects textured with contact maps and 375K frames of synchronized RGB-D+thermal images. To the best of our knowledge, this is the first large-scale dataset that records detailed contact maps for human grasps. Analysis of this data shows the influence of functional intent and object size on grasping, the tendency to touch/avoid 'active areas', and the high frequency of palm and proximal finger contact. Finally, we train state-of-the-art image translation and 3D convolution algorithms to predict diverse contact patterns from object shape. Data, code and models are available at https://contactdb.cc.gatech.edu.

show abstract

Section: Datasets Of Human Graspsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging

Brahmbhatt

Ham

Kemp

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

164

102

View full text Add to dashboard Cite

show abstract

“…Those studies have focused on uncovering more than the dichotomy between power and precision grasps, and they go deep into the way fingers secure objects contained within the hand. Grasping taxonomies have greatly aided in grasp planning for robot manipulation [9], [10], [11]. To some degree, this relates to the theory of affordance [12] where we can infer the functionality of an object based on properties of the object itself.…”

Section: Introductionmentioning

confidence: 99%

Manipulation Motion Taxonomy and Coding for Robots

Paulius

Huang

Meloncon

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Self Cite

View full text Add to dashboard Cite

This paper introduces a taxonomy of manipulations as seen especially in cooking for 1) grouping manipulations from the robotics point of view, 2) consolidating aliases and removing ambiguity for motion types, and 3) provide a path to transferring learned manipulations to new unlearned manipulations. Using instructional videos as a reference, we selected a list of common manipulation motions seen in cooking activities grouped into similar motions based on several trajectory and contact attributes. Manipulation codes are then developed based on the taxonomy attributes to represent the manipulation motions. The manipulation taxonomy is then used for comparing motion data in the Daily Interactive Manipulation (DIM) data set to reveal their motion similarities.

show abstract

“…This specific motion is known as "functional motion". A number of robot grasping strategies [1], [2] are already proposed. In [1], how robot grasping can gain knowledge from human grasping approach is discussed.…”

Section: Introductionmentioning

confidence: 99%

Cooking State Recognition from Images Using Inception Architecture

Salekin

Jelodar

Kushol

2019

2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

View full text Add to dashboard Cite

A kitchen robot properly needs to understand the cooking environment to continue any cooking activities. But object's state detection has not been researched well so far as like object detection. In this paper, we propose a deep learning approach to identify different cooking states from images for a kitchen robot. In our research, we investigate particularly the performance of Inception architecture and propose a modified architecture based on Inception model to classify different cooking states. The model is analyzed robustly in terms of different layers, and optimizers. Experimental results on a cooking dataset demonstrate that proposed model can be a potential solution to the cooking state recognition problem.

show abstract

Grasp planning based on strategy extracted from demonstration

Cited by 23 publications

References 32 publications

ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging

ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging

Manipulation Motion Taxonomy and Coding for Robots

Cooking State Recognition from Images Using Inception Architecture

Contact Info

Product

Resources

About