Catching Objects in Flight

Kim, Seungsu; Shukla, Ashwini; Billard, Aude

doi:10.1109/tro.2014.2316022

Cited by 198 publications

(138 citation statements)

References 43 publications

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“…More recently, Kim et al (2014) presented an impressive programming-by-demonstration approach to catching thrown objects. The system learns object flight dynamics by observing examples, and learns a distribution of feasible grasps from several human-demonstrated grasps on each object.…”

Section: Related Workmentioning

confidence: 99%

Dynamic grasp and trajectory planning for moving objects

et al. 2018

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This paper shows how a robot arm can follow and grasp moving objects tracked by a vision system, as is needed when a human hands over an object to the robot during collaborative working. While the object is being arbitrarily moved by the human co-worker, a set of likely grasps, generated by a learned grasp planner, are evaluated online to generate a feasible grasp with respect to both: the current configuration of the robot respecting the target grasp; and the constraints of finding a collision-free trajectory to reach that configuration. A task-based cost function enables relaxation of motion-planning constraints, enabling the robot to continue following the object by maintaining its end-effector near to a likely pre-grasp position throughout the object's motion. We propose a method of dynamic switching between: a local planner, where the hand smoothly tracks the object, maintaining a steady relative pre-grasp pose; and a global planner, which rapidly moves the hand to a new grasp on a completely different part of the object, if the previous graspable part becomes unreachable. Various experiments are conducted using a real collaborative robot and the obtained results are discussed.

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Section: Related Workmentioning

confidence: 99%

Dynamic grasp and trajectory planning for moving objects

et al. 2018

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“…The measurement or estimation of velocity, for example, by differentiation of a consistent stream of positions, removes the ambiguity of Fig. 1(b) and allows for the realization of online algorithms that cope with very fast dynamics [10,11]. Such methods, however, rely on a planned environment free from occlusions and fast tracking capabilities; requirements difficult to achieve in environments where semi-autonomous robots are expected to make their biggest impact, such as in small factories, hospitals and home care facilities.…”

Section: Related Workmentioning

confidence: 99%

A Probabilistic Framework for Semi-autonomous Robots Based on Interaction Primitives with Phase Estimation

Maeda

Neumann

Ewerton

et al. 2017

Springer Proceedings in Advanced Robotics

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This paper proposes an interaction learning method suited for semi-autonomous robots that work with or assist a human partner. The method aims at generating a collaborative trajectory of the robot as a function of the current action of the human. The trajectory generation is based on action recognition and prediction of the human movement given intermittent observations of his/her positions under unknown speeds of execution; a problem typically found when using motion capture systems in scenarios that lead to occlusion. Of particular interest, the ability to predict the human movement while observing the initial part of his/her trajectory allows for faster robot reactions, and as it will be shown, also eliminates the need of time-alignment of the training data. The method models the coupling between human-robot movement primitives and is scalable in relation to the number of tasks. We evaluated the method using a 7-DoF lightweight robot arm equipped with a 5-finger hand in a multi-task collaborative assembly experiment, also comparing results with our previous method based on timealigned trajectories.

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“…Faster cameras have also been limited to physical applications and not considered for social interaction studies. For instance, motion capture cameras can achieve lower latencies and higher framerates and are often used for physical applications where timing is important (e.g., catching objects (Kim et al, 2014)). New event-based sensors such as the Dynamic Vision Sensor (DVS) (Lichtsteiner et al, 2008;Thorpe, 2012) can allow the detection of events at smaller timescales.…”

Section: Social Robots Require Sensitivity To Events At Very Short Timentioning

confidence: 99%

Social Moments: A Perspective on Interaction for Social Robotics

2017

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During a social interaction, events that happen at different timescales can indicate social meanings. In order to socially engage with humans, robots will need to be able to comprehend and manipulate the social meanings that are associated with these events. We define social moments as events that occur within a social interaction and which can signify a pragmatic or semantic meaning. A challenge for social robots is recognizing social moments that occur on short timescales, which can be on the order of 10 2 ms. In this perspective, we propose that understanding the range and roles of social moments in a social interaction and implementing social micro-abilities-the abilities required to engage in a timely manner through social moments-is a key challenge for the field of human robot interaction (HRI) to enable effective social interactions and social robots. In particular, it is an open question how social moments can acquire their associated meanings. Practically, the implementation of these social micro-abilities presents engineering challenges for the fields of HRI and social robotics, including performing processing of sensors and using actuators to meet fast timescales. We present a key challenge of social moments as integration of social stimuli across multiple timescales and modalities. We present the neural basis for human comprehension of social moments and review current literature related to social moments and social micro-abilities. We discuss the requirements for social micro-abilities, how these abilities can enable more natural social robots, and how to address the engineering challenges associated with social moments.

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Catching Objects in Flight

Cited by 198 publications

References 43 publications

Dynamic grasp and trajectory planning for moving objects

Dynamic grasp and trajectory planning for moving objects

A Probabilistic Framework for Semi-autonomous Robots Based on Interaction Primitives with Phase Estimation

Social Moments: A Perspective on Interaction for Social Robotics

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