Effects of Robot Motion on Human-Robot Collaboration

By Anca Dragan, Shira Bauman, Jodi L. Forlizzi, Siddhartha S. Srinivasa. IEEE/ACM International Conference on Human-Robot Interaction, 2015

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A Survey of Methods for Safe Human-Robot Interaction (2017, methodology): “Elfring et al. (2014), for example, developed a two-step approach incorporating growing hidden Markov models (GHMMs) and the social forces method. During the learning step of this approach, the system uses GHMMs to learn typical human walking patterns from collected data, allowing for continuous upd”
Initiative in robot assistance during collaborative task execution (2016, background): “Another vein of research focuses on low-level motion planning for the robot within a collaborative context [5], [8], [9], [10], with an eye towards improving team fluency and the user’s sense of safety.”
Explainable Agents and Robots: Results from a Systematic Literature Review (2019, neutral): “Intent communication [3] [98] [85] [13] [70] [25] [92] [93] [31] [78] [17] [52]”
An implemented theory of mind to improve human-robot shared plans execution (2016, background): “Human activities can be fairly well monitored ([16], [17]), robot actions can be executed taking into account the humans in the environment ([18], [19]) and human-aware plans can be computed ([2], [3]).”
Evaluating Fluency in Human–Robot Collaboration (2019, background): “Dragan et al. measured ﬂuency for three different paths of motion in a coffee-making scenario [16].”