Construction of an object manipulation database from grasp demonstrations

Kent, David; Chernova, Sonia

doi:10.1109/iros.2014.6943028

Cited by 8 publications

(5 citation statements)

References 14 publications

(18 reference statements)

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“…LfD has been studied to teach manipulators a variety of skills related to manufacturing since the 1980s (156,157). Popular examples include pick and place (79), peg insertion (116), polishing (158), grasping (33,34,159), and assembly operations (7,38,39). The most common approach for introducing demonstrations in manufacturing applications is through kinesthetic teaching.…”

Section: Manufacturingmentioning

confidence: 99%

Recent Advances in Robot Learning from Demonstration

Ravichandar

Polydoros

Chernova

et al. 2020

Annu. Rev. Control Robot. Auton. Syst.

Self Cite

433

219

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In the context of robotics and automation, learning from demonstration (LfD) is the paradigm in which robots acquire new skills by learning to imitate an expert. The choice of LfD over other robot learning methods is compelling when ideal behavior can be neither easily scripted (as is done in traditional robot programming) nor easily defined as an optimization problem, but can be demonstrated. While there have been multiple surveys of this field in the past, there is a need for a new one given the considerable growth in the number of publications in recent years. This review aims to provide an overview of the collection of machine-learning methods used to enable a robot to learn from and imitate a teacher. We focus on recent advancements in the field and present an updated taxonomy and characterization of existing methods. We also discuss mature and emerging application areas for LfD and highlight the significant challenges that remain to be overcome both in theory and in practice.

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Section: Manufacturingmentioning

confidence: 99%

Recent Advances in Robot Learning from Demonstration

Ravichandar

Polydoros

Chernova

et al. 2020

Annu. Rev. Control Robot. Auton. Syst.

Self Cite

433

219

View full text Add to dashboard Cite

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“…Kleinhans et al reported on an ongoing project which acquires RGB-Depth images along with geometric description of objects to create a database of successful and failed grasps 14 . To benefit from crowdsourcing in data generation, Kent and Chernova introduced a user-friendly and intuitive web-based interface for grasp learning by demonstration 15 .…”

Section: Background and Summarymentioning

confidence: 99%

Human grasping database for activities of daily living with depth, color and kinematic data streams

et al. 2018

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This paper presents a grasping database collected from multiple human subjects for activities of daily living in unstructured environments. The main strength of this database is the use of three different sensing modalities: color images from a head-mounted action camera, distance data from a depth sensor on the dominant arm and upper body kinematic data acquired from an inertial motion capture suit. 3826 grasps were identified in the data collected during 9-hours of experiments. The grasps were grouped according to a hierarchical taxonomy into 35 different grasp types. The database contains information related to each grasp and associated sensor data acquired from the three sensor modalities. We also provide our data annotation software written in Matlab as an open-source tool. The size of the database is 172 GB. We believe this database can be used as a stepping stone to develop big data and machine learning techniques for grasping and manipulation with potential applications in rehabilitation robotics and intelligent automation.

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“…Cloud resources can facilitate incremental learning of grasp strategies [40], [120] by matching sensor data against 3D CAD models in an online database. Examples of sensor data include 2D image features [75], 3D features [68], and 3D point clouds [39], as well as demonstrations [94].…”

Section: Big Datamentioning

confidence: 99%

A Survey of Research on Cloud Robotics and Automation

Kehoe

Patil

Abbeel

et al. 2015

IEEE Trans. Automat. Sci. Eng.

706

326

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The Cloud infrastructure and its extensive set of Internet-accessible resources has potential to provide significant benefits to robots and automation systems. We consider robots and automation systems that rely on data or code from a network to support their operation, i.e., where not all sensing, computation, and memory is integrated into a standalone system. This survey is organized around four potential benefits of the Cloud: 1) Big Data: access to libraries of images, maps, trajectories, and descriptive data; 2) Cloud Computing: access to parallel grid computing on demand for statistical analysis, learning, and motion planning; 3) Collective Robot Learning: robots sharing trajectories, control policies, and outcomes; and 4) Human Computation: use of crowdsourcing to tap human skills for analyzing images and video, classification, learning, and error recovery. The Cloud can also improve robots and automation systems by providing access to: a) datasets, publications, models, benchmarks, and simulation tools; b) open competitions for designs and systems; and c) open-source software. This survey includes over 150 references on results and open challenges. A website with new developments and updates is available at: http://goldberg.berkeley.edu/cloud-robotics/ Note to Practitioners-Most robots and automation systems still operate independently using onboard computation, memory, and programming. Emerging advances and the increasing availability of networking in the "Cloud" suggests new approaches where processing is performed remotely with access to dynamic global datasets to support a range of functions. This paper surveys research to date.

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Construction of an object manipulation database from grasp demonstrations

Cited by 8 publications

References 14 publications

Recent Advances in Robot Learning from Demonstration

Recent Advances in Robot Learning from Demonstration

Human grasping database for activities of daily living with depth, color and kinematic data streams

A Survey of Research on Cloud Robotics and Automation

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