To walk efficiently over complex terrain, humans must use vision to tailor their gait to the upcoming ground surface without interfering with the exploitation of passive mechanical forces. We propose that walkers use visual information to initialize the mechanical state of the body before the beginning of each step so the resulting ballistic trajectory of the walker's center-of-mass will facilitate stepping on target footholds. Using a precision stepping task and synchronizing target visibility to the gait cycle, we empirically validated two predictions derived from this strategy: (1) Walkers must have information about upcoming footholds during the second half of the preceding step, and (2) foot placement is guided by information about the position of the target foothold relative to the preceding base of support. We conclude that active and passive modes of control work synergistically to allow walkers to negotiate complex terrain with efficiency, stability, and precision.H umans and other animals are remarkable in their ability to take advantage of what is freely available in the environment to the benefit of efficiency, stability, and coordination in movement. This opportunism can take on at least two forms, both of which are evident in human locomotion over complex terrain: (i) harnessing external forces to minimize the need for self-generated (i.e., muscular) forces (1), and (ii) taking advantage of passive stability to simplify the control of a complex movement (e.g., ref. 2). In the ensuing section, we explain how walkers exploit external forces and passive stability while walking over flat, obstacle-free terrain.* We then generalize this account to walking over irregular surfaces by explaining how walkers can adapt gait to terrain variations while still reaping the benefits of the available mechanical forces and inherent stability. This account leads to hypotheses about how and when walkers use visual information about the upcoming terrain and where that information is found. We derive several predictions from these hypotheses and then put them to the test in three experiments. Passive Control in Human WalkingThe basic movement pattern of the human gait cycle arises primarily from the phasic activation of flexor and extensor muscle groups by spinal-level central pattern generators, regulated by sensory signals from lower limb proprioceptors and cutaneous feedback from the plantar surface of the foot. This low-level neuromuscular circuitry serves to maintain the rhythmic physical oscillations that define locomotor behavior (see ref. 3 for review). This section will provide an overview of the basic biomechanics of the bipedal gait cycle to show how these inherent physical dynamics contribute to the passive stability and energetic efficiency of human locomotion.During the single support phase of the bipedal gait cycle, when only one foot is in contact with the ground, a walker shares the physical dynamics of an inverted pendulum. The body's center of mass (COM) acts as the bob of the pendulum and is support...
The aim of this study was to examine how visual information is used to control stepping during locomotion over terrain that demands precision in the placement of the feet. More specifically, we sought to determine the point in the gait cycle at which visual information about a target is no longer needed to guide accurate foot placement. Subjects walked along a path while stepping as accurately as possible on a series of small, irregularly spaced target footholds. In various conditions, each of the targets became invisible either during the step to the target or during the step to the previous target. We found that making targets invisible after toe off of the step to the target had little to no effect on stepping accuracy. However, when targets disappeared during the step to the previous target, foot placement became less accurate and more variable. The findings suggest that visual information about a target is used prior to initiation of the step to that target but is not needed to continuously guide the foot throughout the swing phase. We propose that this style of control is rooted in the biomechanics of walking, which facilitates an energetically efficient strategy in which visual information is primarily used to initialize the mechanical state of the body leading into a ballistic movement toward the target foothold. Taken together with previous studies, the findings suggest the availability of visual information about the terrain near a particular step is most essential during the latter half of the preceding step, which constitutes a critical control phase in the bipedal gait cycle.
We examine the theoretical understanding of visual gait regulation that has emerged from decades of research since the publication of Lee, Lishman, and Thompson's (1982) classic study of elite long jumpers. The first round of research identified specific informational variables, parameters of the action system, and laws of control that capture the coupling of perception and action in this context, but left unanswered important questions about why visual information is sampled in an intermittent manner and how the strategies that actors adopt ensure stability and energetic efficiency. More recent developments lead to a refined view according to which visual information is used at a specific phase of the gait cycle to modify the parameters that govern the passive dynamics of the body. We then present the results of a new experiment designed to test the prediction that when the terrain offers multiple foothold options for a given step, walkers' choices will be constrained by a strong preference for not interfering with the natural, ballistic movement of the body throughout the single support phase of that step. The findings are consistent with this prediction and support a view of visual gait regulation that is concordant with contemporary accounts of how actors use both active and passive modes of control. (PsycINFO Database Record
The present study investigated differences in the pickup of information about the size and location of an obstacle in the path of locomotion. The main hypothesis was that information about obstacle location is most useful when it is sampled at a specific time during the approach phase, whereas information about obstacle size can be sampled at any point during the last few steps. Subjects approached and stepped over obstacles in a virtual environment viewed through a head-mounted display. In Experiment 1, a horizontal line on the ground indicating obstacle location was visible throughout the trial while information about obstacle height and depth was available only while the subject was passing through a viewing window located at one of four locations along the subject’s path. Subjects exhibited more cautious behavior when the obstacle did not become visible until they were within one step length, but walking behavior was at most weakly affected in the other viewing window conditions. In Experiment 2, the horizontal line indicating obstacle location was removed, such that no information about the obstacle (size or location) was available outside of the viewing window. Subjects adopted a more cautious strategy compared to Experiment 1 and differences between the viewing window conditions and the full vision control condition were observed across several measures. The differences in walking behavior and performance across the two experiments support the hypothesis that walkers have greater flexibility in when they can sample information about obstacle size compared to location. Such flexibility may impact gaze and locomotor control strategies, especially in more complex environments with multiple objects and obstacles.
Multi-agent reinforcement learning (RL) has important implications for the future of human-agent teaming. We show that improved performance with multi-agent RL is not a guarantee of the collaborative behavior thought to be important for solving multi-agent tasks. To address this, we present a novel approach for quantitatively assessing collaboration in continuous spatial tasks with multi-agent RL. Such a metric is useful for measuring collaboration between computational agents and may serve as a training signal for collaboration in future RL paradigms involving humans.
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