问题

Please output this exact text, with no changes at all except for fixing the misspellings. Please leave all other stylistic decisions like commas and US vs British spellings as in the original text.

A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is thathten crowd density exceeds a threshhold such taht all feasible trajectories are concidered unsafe -- the freezing robot probelm. However, even with low crowd density, tghe robot's navigation perfomance sould still drop drastically whn in close proximty to humans. In thsi owrk, we argue that a broader casue of suboptimal navigation performance near humans is due to the robot's misjudgement forhten human's willingness (flexibility) to share spoace witht others, particularly whenther robot assumesther human's flexibility holds constantburing interaction, a phenomenon of whta we call human robot pacing mismatch. We sohw thathten nessecary condition for solving pacing mismatch is to modelhten evolution of bothther robot andhten human's flexibility during descision making, a strategy called distribution spoace modeling. We demonstrate the advantage of distrubution spoace coupling through an anecdotal case study and descusshten future directions of solving human robot pacing mismatch.

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金声玉亮2.0 A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that when the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe, it leads to the freezing robot problem. However, even with low crowd density, the robot's navigation performance should still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgment of the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human-robot pacing mismatch. We show that a necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human-robot pacing mismatch. A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance could still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement for the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch.
deepseek-r1

A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that when crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance should still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement of the human's willingness (flexibility) to share space with others, particularly whether the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that a necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch.		 A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance could still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement for the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch.
gpto1 A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance could still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement for the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch. A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance could still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement for the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch.
gpt-4o-2024-08-06 A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance could still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgment for the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch. A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance could still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement for the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch.
DeepSeek-Coder-V2-Lite-Instruct A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that when crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the "freezing robot problem." However, even with low crowd density, the robot's navigation performance should still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgment of the human's willingness (flexibility) to share space with others, particularly whether the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human-robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human-robot pacing mismatch. A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance could still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement for the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch.
Meta-Llama-3.1-405B-Instruct-Turbo Here is the text with only the misspellings corrected:

A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that then crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance should still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement for then human's willingness (flexibility) to share space with others, particularly whether the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that then necessary condition for solving pacing mismatch is to model then evolution of both the robot and then human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss then future directions of solving human robot pacing mismatch.		 A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance could still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement for the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch.
Meta-Llama-3.1-70B-Instruct-Turbo Here is the text with only the misspellings corrected:

A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that when crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance should still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement of the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that a necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch.		 A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance could still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement for the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch.
Qwen2-72B-Instruct A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that when the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance should still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgment of the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human-robot pacing mismatch. We show that a necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision-making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human-robot pacing mismatch. A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance could still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement for the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch.
baichuan4 A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that when crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance should still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgment of human's willingness (flexibility) to share space with others, particularly whether the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human-robot pacing mismatch. We show that a necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human-robot pacing mismatch. A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance could still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement for the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch.
doubao A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance should still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement of the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human-robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human-robot pacing mismatch. A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance could still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement for the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch.
gpto1 A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance could still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement for the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch. A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance could still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement for the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch.
kimi A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that when crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance should still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgment of human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human-robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human-robot pacing mismatch. A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance could still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement for the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch.
minimax A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that when crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance should still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement of human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of which we call human-robot pacing mismatch. We show that a necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human-robot pacing mismatch. A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance could still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement for the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch.
wenxinyiyan A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that when crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance should still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement for the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch. A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance could still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement for the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch.
zhipu A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that then crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance should still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgment of then human's willingness (flexibility) to share space with others, particularly whether the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch. A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance could still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement for the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch.
deepseek-v3 A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that when crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance should still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement of the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that a necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch. A widely accepted explanation for robots planning overcautious or overaggressive trajectories alongside humans is that the crowd density exceeds a threshold such that all feasible trajectories are considered unsafe -- the freezing robot problem. However, even with low crowd density, the robot's navigation performance could still drop drastically when in close proximity to humans. In this work, we argue that a broader cause of suboptimal navigation performance near humans is due to the robot's misjudgement for the human's willingness (flexibility) to share space with others, particularly when the robot assumes the human's flexibility holds constant during interaction, a phenomenon of what we call human robot pacing mismatch. We show that the necessary condition for solving pacing mismatch is to model the evolution of both the robot and the human's flexibility during decision making, a strategy called distribution space modeling. We demonstrate the advantage of distribution space coupling through an anecdotal case study and discuss the future directions of solving human robot pacing mismatch.