The research was carried out by a team from the Vision and Action Laboratory, the Visual Perception and Cognition Laboratory, and the Cognitive Neurotechnology Unit in the Department of Computer Science and Engineering, led by Associate Professor Hideki Tamura. Earlier work had already established that people feel greater discomfort and prefer larger interpersonal distances when a robot approaches from behind rather than from in front. What remained unknown was whether the perceived speed of an approaching robot itself changes with the direction of approach. This question matters because speed perception sits at the heart of how we judge danger. If a robot behind us seems to be moving faster than it really is, we may experience it as more threatening, react more abruptly, or trust shared spaces with robots less overall. To find out, the team turned to rigorous psychophysical methods, the gold standard for measuring how humans perceive sensory stimuli.

The study comprised five experiments, and the first one set the template. A real autonomous mobile robot, or AMR, approached participants either from the front or from behind. Before each trial, participants memorized two reference speeds: 0.5 meters per second and 1.0 meters per second. They then watched the robot approach at one of seven speeds between those two values and judged whether the approach felt closer to the slower or the faster reference. This two-alternative judgment allowed the researchers to compute a precise perceptual estimate for each direction of approach, rather than relying on vague subjective reports. The setup, in which participants had to turn around to view a robot approaching from behind, was designed to mirror the real-world situation of a pedestrian being overtaken on a sidewalk.

The results were unambiguous. Even when the robot moved at exactly the same physical speed, participants consistently perceived approaches from behind as faster than frontal approaches. What makes the finding remarkable is its consistency: across all five experiments, which combined the real robot with immersive virtual reality simulations, 92 percent of participants exhibited the same perceptual bias. In perception research, where individual differences are often large, a bias shared by nearly every participant is a strong signal that something fundamental about the visual system is at work. The effect was not a marginal statistical trend but a robust feature of how humans experience approaching motion.

Having established the bias, the team then set out to rule out rival explanations. Could it simply be that people learn speeds differently depending on their body orientation, or that the spatial relationship between observer and robot distorts judgment? Could the robot’s appearance differ when seen from behind, or could noise in the observers’ own motor system, involved in turning around, contaminate their estimates? One by one, the subsequent experiments tested and eliminated these alternatives. The evidence instead pointed to vision-dependent factors that arise specifically when observers turn around to view an object approaching from behind. In other words, the act of rotating the head and body to monitor something at our back appears to change how the visual system computes approach speed, a conclusion that narrows the search for the underlying mechanism to perceptual processing rather than peripheral artifacts.

The theoretical interest of the finding is matched by its practical urgency. Robot speed has traditionally been treated as a purely physical quantity, a number of meters per second entered into a navigation algorithm. But if humans systematically overestimate the speed of robots approaching from behind, then a robot that is objectively safe may still feel alarming to the person it overtakes. Tatsuto Yamauchi, the study’s first author and a second-year Ph.D. student in the Department of Computer Science and Engineering, captured this shift in thinking. Robot speed, he noted, has traditionally been considered only in terms of its physical value, such as how many meters per second a robot travels. The findings show that humans perceive the same physical speed differently depending on the direction from which the robot approaches, and as autonomous mobile robots become increasingly common in everyday environments, designing robot motion based not only on physical safety but also on how humans actually perceive robot speed will contribute to safer, more comfortable, and more acceptable human-robot coexistence.

To translate the perceptual data into engineering practice, the researchers embedded the experimentally observed bias into a robot navigation model and simulated a scenario in which a robot overtakes a pedestrian from behind, one of the most common and most delicate maneuvers in shared human-robot spaces. The comparison against a conventional navigation model was revealing. When the perceptual bias was corrected for, the robot initiated its avoidance maneuver earlier and generated trajectories that maintained a greater distance from the pedestrian. The numbers are concrete: when the largest experimentally observed perceptual bias was applied, the robot passed up to 0.40 meters farther away from the pedestrian and began its avoidance 0.57 seconds earlier than the baseline model. In the world of pedestrian robotics, where clearance distances are often measured in tens of centimeters, both figures represent a substantial margin of perceived safety.

This approach belongs to an emerging philosophy sometimes called perception-aware robot motion design. Rather than treating the human observer as a passive obstacle to be avoided, perception-aware algorithms treat human perception itself as a variable to be modeled and respected. The Toyohashi study provides exactly the kind of quantitative perceptual data such algorithms need. Because the bias was measured psychophysically with a real robot and validated in virtual reality, it can be parameterized and inserted directly into trajectory planners. The simulation result demonstrates the payoff: a robot that knows humans overestimate rear-approach speeds can compensate preemptively, slowing down, widening its path, or beginning its maneuver sooner, without any change to its physical safety envelope. The robot is not actually more dangerous; it simply behaves as though the human’s perception were the ground truth, because for the human’s experience, it is.

The authors are careful to frame the current work as a beginning rather than an endpoint. The study focused on the perception of approach speed itself, and future research is needed to determine how the bias influences subjective comfort, perceived safety, and actual avoidance behavior during real human-robot interactions. It also remains to be seen whether the effect holds at speeds outside the tested range of 0.5 to 1.0 meters per second, and whether robots of different sizes and shapes produce the same perceptual distortion. These open questions are practically important, since delivery robots, service robots, and industrial AMRs operate across a wide range of speeds and form factors. Still, the core message is already actionable. The same physical motion is not the same perceived motion, and the direction from which a machine arrives changes how fast it seems to move. As robots and humans increasingly share sidewalks, corridors, and public spaces, the machines that feel safest may be the ones designed around the quirks of human vision rather than around physics alone.

Subject of Research: Perceptual bias in human speed perception of robots approaching from behind and its implications for robot navigation design

Article Title: Robots approaching from behind are perceived as moving faster

Article References: Robots approaching from behind are perceived as moving faster. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: autonomous mobile robots, speed perception, psychophysics, human-robot interaction, virtual reality, robot navigation, perception-aware motion design, Toyohashi University of Technology, IEEE Robotics and Automation Letters, pedestrian safety, visual perception, motion perception

Fast forward ten years, the focus at SUTD has drastically shifted. Rather than pursuing further efficiency improvements for the multi-rotor design, Assoc Prof Foong and his research team looked towards nature for inspiration, specifically the aerodynamic prowess of the samara—those spiral-winged seeds released by maple trees. Embracing a minimalist approach, they engineered a lightweight monocopter that utilizes a single actuator to enable efficient and controllable flight.

In their study published in the prestigious IEEE Robotics and Automation Letters, titled “Design and optimization of a samara-inspired lightweight monocopter for extended endurance,” the researchers disclosed their meticulous design process. The outcome is astonishing: a drone weighing only 32 grams capable of hovering autonomously for 26 minutes. This achievement far exceeds the performance metrics of typical drones within the same weight class, thereby challenging conventional understandings of drone performance.

As Assoc Prof Foong elaborated, the rationale behind this design transformation is rooted in the inherent inefficiencies associated with scaling down technologies. Small drones often succumb to poor endurance primarily due to the limitations of their small propellers, which generate finite thrust at high power consumption. Their innovative monocopter design addresses these limitations by adopting principles observed in nature, effectively reorienting drone design philosophy towards maximizing efficiency rather than merely increasing power.

Nature’s samaras utilize a clever approach, leveraging passive stability while crafting an aerodynamically efficient flight characteristic. Assoc Prof Foong emphasized that every portion of the maple seed contributes to its lift—an insight that was instrumental in developing an airframe design where each component serves a purpose without redundancy. This leads to a design ethos that champions resource efficiency and simplicity over excessive intricacy.

The integration of artificial intelligence has also played a vital role in this engineering endeavor. While inspiration was drawn from the natural world, the optimisation of wing geometries and balancing mass distribution was significantly enhanced through advanced AI-driven methodologies. Such techniques allow researchers to rigorously explore various design configurations efficiently, sidestepping the need for exhaustive manual testing.

Unlike traditional quadcopters that depend on multiple rotors for flight, the monocopter developed by the SUTD team demonstrates that a solo actuator suffices. This pivotal component spins the winged body to maintain stability via passive dynamics while simultaneously generating lift through the drone’s expansive airfoil design. The resulting structural integrity reflects a profound simplicity that belies its effective performance.

The optimization process behind the monocopter was exhaustive and intricately detailed. Combining classical aerodynamic theories with contemporary real-world performance models, researchers employed a surrogate optimization method, a cutting-edge data-driven algorithm to refine every aspect of the monocopter’s design. Ultimately, their efforts yielded a drone with a power loading ratio of 9.1 grams per watt, setting new standards for micro air vehicles in terms of both size and hovering capability.

Research Fellow Cai Xinyu, who contributed to the drone’s development, expressed the excitement of their achievement, noting the viability of small aerial robotics achieving endurance benchmarks traditionally expected from larger systems. This breakthrough demonstrates that advancement in drone technology does not solely hinge on increased size but can be achieved through refined engineering and intelligent design practices.

The potential applications for this monocopter are vast and varied. Its lightweight and enduring characteristics herald possibilities in low-cost, long-duration missions. Among these is the exciting proposition of a resilient, reusable radiosonde— a weather-monitoring instrument traditionally deployed via balloons. This innovative approach recently garnered recognition as the Sustainability Winner at the James Dyson Award for 2024, further validating the environmental impact and practical utility of this aerial technology.

While the current prototype leverages readily available commercial components, there are ambitious plans for the future. Research teams are aiming to explore bespoke materials to enhance the performance of the monocopter further while broadening its payload capacity and extending flight duration without significantly augmenting its weight. The evolving design will also consider bio-inspired morphologies, ensuring that every innovation continues to push the limits of drone capabilities.

The story of the SG60 monocopter encapsulates an impressive technological journey that illustrates not only continuity but a significant departure from traditional drone engineering paradigms. With aspirations set high, the SUTD team envisions developing a prototype capable of achieving over 60 minutes of flight duration, aptly commemorating Singapore’s upcoming 60th anniversary. In an emphatic statement, Assoc Prof Foong summarized their mission by highlighting that their work encapsulates not just technological innovation but a sustainable vision for the future of aerial robotics.

In summary, the development of the samara-inspired monocopter signifies a formidably innovative leap in drone technology, underpinned by a rigorous research approach, clear inspiration from nature, and the impactful integration of artificial intelligence into the design process. This project stands as a testament to the potential of biomimicry and advanced engineering in transcending previously understood limitations of drone technologies, providing a vision that challenges the conventions of scale and efficiency.

Subject of Research: Samara-Inspired Monocopter Design
Article Title: Small but mighty: A biomimetic idea takes flight
News Publication Date: October 2023
Web References: IEE Robotics and Automation Letters
References: N/A
Image Credits: Credit: SUTD

Keywords

Biomimetic drones, monocopter design, drone technology, lightweight drones, SUTD, AI in design, sustainable engineering.