Collaboration of heterogeneous agents in time constrained tasks

Maniadakis, Michail; Aksoy, Eren Erdal; Asfour, Tamim; Trahanias, Panos

doi:10.1109/humanoids.2016.7803314

Cited by 9 publications

(11 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To address the issues mentioned above, we have recently introduced the Daisy Planner (DP) (Maniadakis et al, 2016a , b ), a new scheme of time-informed planning, which relies on the daisy representation of tasks and adopts time-inclusive multi-criteria ranking of alternative plans. DP operates under the assumption of pursuing immediate, locally optimal assignment of tasks to agents.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The fluent coordination of multi-agent activities plays a crucial role in the joint accomplishment of goals. We have recently introduced (Maniadakis et al, 2016a , b ), a time-informed planner that attributes tasks to agents in a step-by-step manner, accomplishing the effective coordination of multiple agents (see also, Isaacson et al, 2019 ). The planner assumes the daisy-like representation of the composite behavior and is thus termed Daisy Planner (DP).…”

Section: Time-informed Multi-agent Interaction Implementationmentioning

confidence: 99%

See 1 more Smart Citation

Time-Aware Multi-Agent Symbiosis

et al. 2020

Self Cite

View full text Add to dashboard Cite

Contemporary research in human-machine symbiosis has mainly concentrated on enhancing relevant sensory, perceptual, and motor capacities, assuming short-term and nearly momentary interaction sessions. Still, human-machine confluence encompasses an inherent temporal dimension that is typically overlooked. The present work shifts the focus on the temporal and long-lasting aspects of symbiotic human-robot interaction (sHRI). We explore the integration of three time-aware modules, each one focusing on a diverse part of the sHRI timeline. Specifically, the Episodic Memory considers past experiences, the Generative Time Models estimate the progress of ongoing activities, and the Daisy Planner devices plans for the timely accomplishment of goals. The integrated system is employed to coordinate the activities of a multi-agent team. Accordingly, the proposed system (i) predicts human preferences based on past experience, (ii) estimates performance profile and task completion time, by monitoring human activity, and (iii) dynamically adapts multi-agent activity plans to changes in expectation and Human-Robot Interaction (HRI) performance. The system is deployed and extensively assessed in real-world and simulated environments. The obtained results suggest that building upon the unfolding and the temporal properties of team tasks can significantly enhance the fluency of sHRI.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Time-informed Multi-agent Interaction Implementationmentioning

confidence: 99%

Time-Aware Multi-Agent Symbiosis

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, we introduced the Daisy Planner, in an attempt to address time-informed planning in multi-agent setups. 9 Below, we summarize the most relevant issues which are essential for the completeness of the present work. The planner uses a fuzzy number representation of time to enable the processing of temporal information and develop time-informed multi-criteria optimized plans.…”

Section: Speed Inference Based On Temporal Informationmentioning

confidence: 99%

“…The proposed spatio-temporal formulation of IMFO readily lends itself to integration with timeinformed planning approaches 9 to effectively address temporal constraint satisfaction in real-world scenarios. Following our previous work on latency estimation, 10 the composite system is capable to cope with cases where completion of certain behaviors is expected to be delayed.…”

Section: Introductionmentioning

confidence: 99%

Speed Adaptation in Learning from Demonstration through Latent Space Formulation

2019

Self Cite

View full text Add to dashboard Cite

SUMMARYPerforming actions in a timely manner is an indispensable aspect in everyday human activities. Accordingly, it has to be present in robotic systems if they are going to seamlessly interact with humans. The current work addresses the problem of learning both the spatial and temporal characteristics of human motions from observation. We formulate learning as a mapping between two worlds (the observed and the action ones). This mapping is realized via an abstract intermediate representation termed “Latent Space.” Learned actions can be subsequently invoked in the context of more complex human–robot interaction (HRI) scenarios. Unlike previous learning from demonstration (LfD) methods that cope only with the spatial features of an action, the formulated scheme effectively encompasses spatial and temporal aspects. Learned actions are reproduced under the high-level control of a time-informed task planner. During the implementation of the studied scenarios, temporal and physical constraints may impose speed adaptations in the reproduced actions. The employed latent space representation readily supports such variations, giving rise to novel actions in the temporal domain. Experimental results demonstrate the effectiveness of the proposed scheme in the implementation of HRI scenarios. Finally, a set of well-defined evaluation metrics are introduced to assess the validity of the proposed approach considering the temporal and spatial consistency of the reproduced behaviors.

show abstract

“…In robotics, time has been well acknowledged as a glue factor for several cognitive skills [5]. Consequently, the timing of robotic actions has been studied in various contexts, including robot-navigation [20], human-robot interaction [3], robot-robot [4] and human-robot teams [4], to name a few. Time is also useful in the context of human robot coordination where studies use the execution time of actions [15], [16] to synchronize two or more agents.…”

Section: Literature Reviewmentioning

confidence: 99%

Online Performance Prediction and Profiling of Human Activities by Observation

Hourdakis

Maniadakis

Trahanias

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Self Cite

View full text Add to dashboard Cite

The ability to predict the duration of an activity can enable a robot to plan its behaviors ahead, interact seamlessly with other humans, by coordinating its actions, and allocate effort and resources to tasks that are time-constrained or critical. Despite its usefulness, models that examine the temporal properties of an activity remain relatively unexplored.In the current paper we present, to the best of our knowledge, the first method that can estimate temporal properties of an activity by observation. We evaluate it on three use-cases (i) wiping a table, (ii) chopping vegetables and (iii) cleaning the floor, using ground truth data from real demonstrations, and show that it can make predictions with high accuracy and little training. In addition, we investigate different methods to approximate the progress of each task, and demonstrate how a model can generalize, by reusing part of it in different activities.

show abstract

Collaboration of heterogeneous agents in time constrained tasks

Cited by 9 publications

References 11 publications

Time-Aware Multi-Agent Symbiosis

Time-Aware Multi-Agent Symbiosis

Speed Adaptation in Learning from Demonstration through Latent Space Formulation

Online Performance Prediction and Profiling of Human Activities by Observation

Contact Info

Product

Resources

About