Human preferences for robot-human hand-over configurations

Cakmak,; Srinivasa,; Lee, Myeong Soo; Forlizzi,; Kiesler,

doi:10.1109/iros.2011.6048340

Cited by 55 publications

(81 citation statements)

References 8 publications

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“…Our application is a significant departure from current work on handovers, which analyze important features of the handover process, like posture, interpersonal distance, arm dynamics, and grip forces [13]- [18]. Our results complement this work by focusing instead on the communication of intent prior to the physical act of the handover.…”

Section: Introductionmentioning

confidence: 80%

Learning the communication of intent prior to physical collaboration

Strabala

Lee

Dragan

et al. 2012

2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication

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Abstract-When performing physical collaboration tasks, like packing a picnic basket together, humans communicate strongly and often subtly via multiple channels like gaze, speech, gestures, movement and posture. Understanding and participating in this communication enables us to predict a physical action rather than react to it, producing seamless collaboration. In this paper, we automatically learn key discriminative features that predict the intent to handover an object using machine learning techniques. We train and test our algorithm on multichannel vision and pose data collected from an extensive user study in an instrumented kitchen. Our algorithm outputs a tree of possibilities, automatically encoding various types of prehandover communication. A surprising outcome is that mutual gaze and inter-personal distance, often cited as being key for interaction, were not key discriminative features. Finally, we discuss the immediate and future impact of this work for human-robot interaction.

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Section: Introductionmentioning

confidence: 80%

Learning the communication of intent prior to physical collaboration

Strabala

Lee

Dragan

et al. 2012

2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication

Self Cite

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“…Moreover, as an imitation learning method, Interaction ProMPs implicitly encode user preferences from demonstrations which seems to be more suited for human interaction than pure motion planning approaches (Cakmak, Srinivasa, Lee, Forlizzi, & Kiesler, 2011).…”

Section: Interaction Promps In the Handover Contextmentioning

confidence: 99%

Probabilistic movement primitives for coordination of multiple human–robot collaborative tasks

et al. 2016

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This paper proposes an interaction learning method for collaborative and assistive robots based on movement primitives. The method allows for both action recognition and human-robot movement coordination. It uses imitation learning to construct a mixture model of human-robot interaction primitives. This probabilistic model allows the assistive trajectory of the robot to be inferred from human observations. The method is scalable in relation to the number of tasks and can learn nonlinear correlations between the trajectories that describe the human-robot interaction. We evaluated the method experimentally with a lightweight robot arm in a variety of assistive scenarios, including the coordinated handover of a bottle to a human, and the collaborative assembly of a toolbox. Potential applications of the method are personal caregiver robots, control of intelligent prosthetic devices, and robot coworkers in factories.Keywords Movement primitives · physical humanrobot interaction · imitation learning · mixture model · action recognition · trajectory generation

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“…Work in Human Robot interaction has focused on using timing to enable better interaction [28], [29]. Researchers have looked at how human motion and gestures vary in timing and shape to help convey intentions better.…”

Section: Related Workmentioning

confidence: 99%

Space-time functional gradient optimization for motion planning

Byravan

Boots

Srinivasa

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Functional gradient algorithms (e.g. CHOMP) have recently shown great promise for producing locally optimal motion for complex many degree-of-freedom robots. A key limitation of such algorithms is the difficulty in incorporating constraints and cost functions that explicitly depend on time. We present T-CHOMP, a functional gradient algorithm that overcomes this limitation by directly optimizing in space-time. We outline a framework for joint space-time optimization, derive an efficient trajectory-wide update for maintaining time monotonicity, and demonstrate the significance of T-CHOMP over CHOMP in several scenarios. By manipulating time, T-CHOMP produces lower-cost trajectories leading to behavior that is meaningfully different from CHOMP.

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Human preferences for robot-human hand-over configurations

Cited by 55 publications

References 8 publications

Learning the communication of intent prior to physical collaboration

Learning the communication of intent prior to physical collaboration

Probabilistic movement primitives for coordination of multiple human–robot collaborative tasks

Space-time functional gradient optimization for motion planning

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