Learning manipulation actions from human demonstrations

Welschehold, Tim; Dornhege, Christian; Burgard, Wolfram

doi:10.1109/iros.2016.7759555

Cited by 20 publications

(10 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For rigid objects, the deformation of objects is negligible. The LfD approach [ 76 , 78 ] could transfer the manipulation skill from human to robot, which enables a robot to manipulate objects with more flexibility. Skill-based programming [ 80 , 81 , 82 , 83 , 84 ] eases the robot program generation and enables the robot to complete varied object manipulation, such as grasping, picking, and assembly.…”

Section: Discussionmentioning

confidence: 99%

“…The CC-LfD not only realizes the robust skill learning but also finishes the skilled repair that incorporates the annotations of conceptually grounded constraints during live demonstrations of the LfD. Furthermore, Welschehold et al [ 78 ] proposed an RGB-D observation-based demonstration approach. This approach prevents the requirement of accurate knowledge about the interactions between the robot and the object, which also eliminates the unintentional operator error.…”

Section: Physical-uncertain Objectsmentioning

confidence: 99%

See 1 more Smart Citation

Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review

Wang

Zhang

Zang

et al. 2020

Sensors

View full text Add to dashboard Cite

As there come to be more applications of intelligent robots, their task object is becoming more varied. However, it is still a challenge for a robot to handle unfamiliar objects. We review the recent work on the feature sensing and robotic grasping of objects with uncertain information. In particular, we focus on how the robot perceives the features of an object, so as to reduce the uncertainty of objects, and how the robot completes object grasping through the learning-based approach when the traditional approach fails. The uncertain information is classified into geometric information and physical information. Based on the type of uncertain information, the object is further classified into three categories, which are geometric-uncertain objects, physical-uncertain objects, and unknown objects. Furthermore, the approaches to the feature sensing and robotic grasping of these objects are presented based on the varied characteristics of each type of object. Finally, we summarize the reviewed approaches for uncertain objects and provide some interesting issues to be more investigated in the future. It is found that the object’s features, such as material and compactness, are difficult to be sensed, and the object grasping approach based on learning networks plays a more important role when the unknown degree of the task object increases.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Physical-uncertain Objectsmentioning

confidence: 99%

Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review

Wang

Zhang

Zang

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…During optimization the teacher's demonstrations are adapted towards trajectories that are feasible for robot execution. For details on the graph structure and the implementation we refer to Welschehold et al [22], [23].…”

Section: Action Learningmentioning

confidence: 99%

3D Human Pose Estimation in RGBD Images for Robotic Task Learning

Zimmermann

Welschehold

Dornhege

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

124

View full text Add to dashboard Cite

We propose an approach to estimate 3D human pose in real world units from a single RGBD image and show that it exceeds performance of monocular 3D pose estimation approaches from color as well as pose estimation exclusively from depth. Our approach builds on robust human keypoint detectors for color images and incorporates depth for lifting into 3D. We combine the system with our learning from demonstration framework to instruct a service robot without the need of markers. Experiments in real world settings demonstrate that our approach enables a PR2 robot to imitate manipulation actions observed from a human teacher.

show abstract

“…Programming by demonstration (PbD) is a typical approach for transferring skills to robots by mirroring human actions (Billard et al, 2008 ; Argall et al, 2009 ; Calinon et al, 2014 ; Peternel et al, 2018 ). For simple tasks, human demonstrations are typically recorded using vision-based systems (Welschehold et al, 2016 ) or motion tracking suites (Filippeschi et al, 2017 ). For a more challenging task where force constraints and compliance strategies are required, the kinesthetic guidance or multi-modal human-in-the-loop skill transfer approaches can be used (Peternel et al, 2014 ; Rozo et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Phase-Synchronized Learning of Periodic Compliant Movement Primitives (P-CMPs)

Petrič

2020

Front. Neurorobot.

View full text Add to dashboard Cite

Autonomous trajectory and torque profile synthesis through modulation and generalization require a database of motion with accompanying dynamics, which is typically difficult and time-consuming to obtain. Inspired by adaptive control strategies, this paper presents a novel method for learning and synthesizing Periodic Compliant Movement Primitives (P-CMPs). P-CMPs combine periodic trajectories encoded as Periodic Dynamic Movement Primitives (P-DMPs) with accompanying task-specific Periodic Torque Primitives (P-TPs). The state-of-the-art approach requires to learn TPs for each variation of the task, e.g., modulation of frequency. Comparatively, in this paper, we propose a novel P-TPs framework, which is both frequency and phase-dependent. Thereby, the executed P-CMPs can be easily modulated, and consequently, the learning rate can be improved. Moreover, both the kinematic and the dynamic profiles are parameterized, thus enabling the representation of skills using corresponding parameters. The proposed framework was evaluated on two robot systems, i.e., Kuka LWR-4 and Franka Emika Panda. The evaluation of the proposed approach on a Kuka LWR-4 robot performing a swinging motion and on Franka Emika Panda performing an exercise for elbow rehabilitation shows fast P-CTPs acquisition and accurate and compliant motion in real-world scenarios.

show abstract

Learning manipulation actions from human demonstrations

Cited by 20 publications

References 17 publications

Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review

Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review

3D Human Pose Estimation in RGBD Images for Robotic Task Learning

Phase-Synchronized Learning of Periodic Compliant Movement Primitives (P-CMPs)

Contact Info

Product

Resources

About