A novel pipeline for bi-manual handover task

Vezzani, Giulia; Regoli, Massimo; Pattacini, Ugo; Natale, Lorenzo

doi:10.1080/01691864.2017.1380535

Cited by 4 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This highlights one of the potential problems with multi-arm coordination in household settings. Robotic hands may have to work very closely together, to the point of touching [197] (see Fig. 14).…”

Section: Coordination and Dexteritymentioning

confidence: 99%

“…The most difficult part of these Fig. 14 A set of snapshots showing successful multi-hands grasping for various objects [197] manipulations is usually not the grasping itself, but the additional manipulation that is necessary when the object is already grasped. Preventing an object from slipping from a gripper [68] and minor manipulation with fingers while grasping [183] have already been somewhat explored.…”

Section: Coordination and Dexteritymentioning

confidence: 99%

See 1 more Smart Citation

The State of Lifelong Learning in Service Robots:

Kasaei

Melsen

Beers

et al. 2021

J Intell Robot Syst

View full text Add to dashboard Cite

Service robots are appearing more and more in our daily life. The development of service robots combines multiple fields of research, from object perception to object manipulation. The state-of-the-art continues to improve to make a proper coupling between object perception and manipulation. This coupling is necessary for service robots not only to perform various tasks in a reasonable amount of time but also to continually adapt to new environments and safely interact with non-expert human users. Nowadays, robots are able to recognize various objects, and quickly plan a collision-free trajectory to grasp a target object in predefined settings. Besides, in most of the cases, there is a reliance on large amounts of training data. Therefore, the knowledge of such robots is fixed after the training phase, and any changes in the environment require complicated, time-consuming, and expensive robot re-programming by human experts. Therefore, these approaches are still too rigid for real-life applications in unstructured environments, where a significant portion of the environment is unknown and cannot be directly sensed or controlled. In such environments, no matter how extensive the training data used for batch learning, a robot will always face new objects. Therefore, apart from batch learning, the robot should be able to continually learn about new object categories and grasp affordances from very few training examples on-site. Moreover, apart from robot self-learning, non-expert users could interactively guide the process of experience acquisition by teaching new concepts, or by correcting insufficient or erroneous concepts. In this way, the robot will constantly learn how to help humans in everyday tasks by gaining more and more experiences without the need for re-programming. In this paper, we review a set of previously published works and discuss advances in service robots from object perception to complex object manipulation and shed light on the current challenges and bottlenecks.

show abstract

Section: Coordination and Dexteritymentioning

confidence: 99%

Section: Coordination and Dexteritymentioning

confidence: 99%

The State of Lifelong Learning in Service Robots:

Kasaei

Melsen

Beers

et al. 2021

J Intell Robot Syst

View full text Add to dashboard Cite

show abstract

“…Lertkultanon et al [23], Zhou et al [24], and Suarez-Rui et al [25] respectively applied integral prehensile and non-prehensile sequential dualarm motion planning to the preparatory manipulation of a chair. Vezzani et al [26] presented a framework for simultaneous handover and visual recognition.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Preparatory Manipulation Planning Using Automatically Determined Single and Dual Arm

Wan

Harada

Kanehiro

2020

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

This paper presents a manipulation planning algorithm for robots to reorient objects. It automatically finds a sequence of robot motion that manipulates and prepares an object for specific tasks. Examples of the preparatory manipulation planning problems include reorienting an electric drill to cut holes, reorienting workpieces for assembly, and reorienting cargo for packing, etc. The proposed algorithm could plan single and dual arm manipulation sequences to solve the problems. The mechanism under the planner is a regrasp graph which encodes grasp configurations and object poses. The algorithms search the graph to find a sequence of robot motion to reorient objects. The planner is able to plan both single and dual arm manipulation. It could also automatically determine whether to use a single arm, dual arms, or their combinations to finish given tasks. The planner is examined by various humanoid robots like Nextage, HRP2Kai, HRP5P, etc., using both simulation and real-world experiments.

show abstract

“…Vezzani et al [11] propose a pipeline for bi-manual handover of known objects. Tactile and visual feedback are used for object pose estimation and grasp stabilization.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Bimanual Functional Regrasping of Novel Object Class Instances

Pavlichenko

Rodríguez

Lenz

et al. 2019

2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids)

View full text Add to dashboard Cite

In human-made scenarios, robots need to be able to fully operate objects in their surroundings, i.e., objects are required to be functionally grasped rather than only picked. This imposes very strict constraints on the object pose such that a direct grasp can be performed. Inspired by the anthropomorphic nature of humanoid robots, we propose an approach that first grasps an object with one hand, obtaining full control over its pose, and performs the functional grasp with the second hand subsequently. Thus, we develop a fully autonomous pipeline for dual-arm functional regrasping of novel familiar objects, i.e., objects never seen before that belong to a known object category, e.g., spray bottles. This process involves semantic segmentation, object pose estimation, non-rigid mesh registration, grasp sampling, handover pose generation and in-hand pose refinement. The latter is used to compensate for the unpredictable object movement during the first grasp. The approach is applied to a human-like upper body. To the best knowledge of the authors, this is the first system that exhibits autonomous bimanual functional regrasping capabilities. We demonstrate that our system yields reliable success rates and can be applied on-line to real-world tasks using only one off-the-shelf RGB-D sensor.

show abstract

A novel pipeline for bi-manual handover task

Cited by 4 publications

References 10 publications

The State of Lifelong Learning in Service Robots:

The State of Lifelong Learning in Service Robots:

Preparatory Manipulation Planning Using Automatically Determined Single and Dual Arm

Autonomous Bimanual Functional Regrasping of Novel Object Class Instances

Contact Info

Product

Resources

About