Ground robots have moved from niche reconnaissance roles to routine components of dismounted operations. This technical maturation creates a parallel, deeper challenge that is less about sensors and actuators and more about the human mind. If we are to field robotic squadmates that actually extend human capability rather than undermine it, we must first confront the psychological dynamics that govern human-robot teams.
At the heart of those dynamics is situation awareness. Situation awareness is not a pithy slogan; it is a cognitive construct composed of perceiving relevant elements in the environment, understanding their meaning, and projecting future states. Effective human-robot teaming depends on both human and machine maintaining complementary forms of situation awareness so the team can anticipate, adapt, and act. When the robot becomes opaque, human partners lose a critical handle on comprehension and projection and decision quality degrades.
Trust is the social lubricant of teaming, but trust in automation is not a binary variable. Classic human factors work shows that designers must aim for appropriate reliance, not maximal trust. Too little trust and operators ignore or underuse capable systems. Too much trust and operators become complacent, failing to monitor or to intervene when the system errs. The engineering problem is therefore simultaneously psychological. Interfaces and modes of interaction should be engineered to support calibrated trust rather than to elicit unconditional confidence.
Two pathological responses recur in the literature and in field reports. The first is complacency, a state where operators reduce vigilance because they assume the automation will handle anomalies. The second is automation bias, a cognitive bias to accept automated suggestions even when they are incorrect. Both phenomena arise from attentional limitations and task load and, critically, they are stubborn: experience and simple training often do not eliminate them. In contexts where soldiers must monitor both a robot and a complex environment, these effects can produce both omission errors and dangerous delays in intervention.
A constructive response to these problems has emerged from human-autonomy teaming research in recent years. The Situation Awareness-based Agent Transparency model proposes that agents present information about their current actions and plans, their reasoning, and their projected outcomes. Experiments with an Autonomous Squad Member style interface found that increasing transparency generally improved operators’ ability to calibrate trust and, in many cases, improved task performance. The effect is not magical; transparency that merely dumps data can increase workload or even distract. The design challenge is to present the right level of reasoning and projection, at the right time, in a way that supports human sensemaking.
Team composition and role clarity also carry psychological weight. Soldiers entering an operation expect predictable norms of responsibility and accountability. Introducing an autonomous or semi-autonomous ground robot disrupts those expectations. Questions arise immediately: who is responsible when an autonomous sensor misses a threat, when the robot’s decision-support nudges a user toward a risky action, or when a remotely supervised system fires on a target? These are not merely legal or moral questions, they are psychological. Ambiguity about responsibility changes human behavior, often in ways that increase risk. Designers and commanders must therefore make roles and override authorities explicit and train personnel under those conditions until the behavioral norms are stable.
There is also a moral terrain to traverse. Technical proposals such as so-called ethical governors attempt to constrain machine behavior to conform to legal and normative constraints. While these constructs are intellectually interesting and show promise as design components, they also externalize moral judgment into software. That shift has psychological consequences for human teammates who may feel relieved of or alienated from responsibility, or conversely, who may resent ceding judgment to an inscrutable algorithm. The emotional and moral dimensions of teaming must be considered alongside classical human factors.
Practical mitigation tactics are straightforward in principle and subtle in execution. First, interface design should prioritize transparency that maps to human cognitive models. Present plans, rationales, and uncertainty in levels that correspond to perception, comprehension, and projection. Second, train teams with realistic, failure-mode-rich exercises so that operators experience automation fallibility and learn when and how to reassert control. Evidence suggests that the nature of failure exposure in training affects later bias and complacency, so training curricula must be designed deliberately. Third, distribute tasks in a manner that manages attentional load; avoid dual-monitoring tasks that force soldiers to split attention between a demanding environmental task and opaque robot status displays. Fourth, codify roles and accountability so psychological expectations about responsibility do not drift in the heat of operations.
Finally, we must accept that technology alone cannot resolve the psychological frictions of teaming. Robots can be made more transparent, and algorithms can estimate and report uncertainty. Yet the human experience of working with machines is shaped by culture, doctrine, training, and emotion. Designers who ignore that broader social context risk producing technically impressive systems that are operationally unusable or, worse, operationally dangerous.
In short, ground robots will only be effective squadmates when engineering and psychology are treated as coequal design constraints. The right level of transparency, thoughtfully structured training, attention-aware task allocation, and clear lines of responsibility form the minimal set of institutional commitments required. Absent those commitments we will alternately see robotic systems abandoned in the field for lack of trust or blamed for failures that were, in truth, failures of human-robot teaming design. Neither outcome is acceptable. The ethical imperative is to build machines that augment human judgment while preserving human responsibility for its use.