The Will Smith 2004 blockbuster I, Robot predicts pervasive humanoid robots in 2035; investors agree, roboticists disagree.
Humanoid robots stand at the forefront of robotics, aiming to capture the agility, robustness, and expressivity of human movement in an anthropomorphic form. The locomotion control of humanoids has evolved from classical model-based methods to reinforcement learning powered by large-scale simulation and now to generative models that produce adaptive, whole-body behaviors, propelling humanoids toward operation in real-world environments. This survey positions humanoid control at a turning point, converging toward a unified paradigm of physics-guided generative intelligence that integrates optimization, learning, and predictive reasoning. We identify three core principles linking these paradigms: physics-based modeling, constrained decision-making, and adaptation to uncertainty. Building on these connections, we provide recommendations for researchers and outline open challenges in safety, accessibility, and human-level capability. These directions represent a transformation from engineered stability to intelligent autonomy, laying the groundwork for humanoid generalists capable of operating safely, collaborating naturally, and extending human capability in the open world.
We are entering an era where AI enables multipurpose applications, but the mechanical hardware must be done right.
The human brain can process spatial action through either egocentric (self-centered) or allocentric (environment-centered) perspectives. Despite this cognitive ability, teleoperation of robotic systems has been predominantly egocentric, wherein operators control a single robot from an inside-out perspective with limited situational overview. This paper proposes a scalable paradigm for simultaneously teleoperating multiple robotic systems from an allocentric perspective that enables a single operator to solve complex collaborative tasks. The perceptual information of all robots is fused into a joint virtual environment, which the operator can view from a top-down perspective to control the robots similar to puppets. We investigated differences between egocentric and allocentric multirobot teleoperation using both simulated and real robots. In the simulation study, study participants (n = 15) reported a higher sense of embodiment over robot bodies when using egocentric control, enabling them to perform more precise manipulation after initial training. Using allocentric control, participants achieved speed increases of 84% in a parallelizable locomotion task and 50% in a situational overview task compared with egocentric control. Participants reported improved system usability and overview with allocentric control, and no significant differences in sense of agency were measured. These results were validated in a real-world study with two physical robots (n = 6) and underscore the effectiveness of our allocentric teleoperation framework as a complementary approach to egocentric robotic teleoperation. Interfaces that support smooth switching between allocentric and egocentric control therefore enable operators to leverage the advantages of both paradigms depending on their specific task.
Enabling quadrupedal robots to traverse complex terrains, from rugged outdoor environments to urban landscapes, requires seamless integration of multiple motor skills, smooth transitions between gaits, and high-speed perceptive locomotion using only onboard sensors. We present APT-RL (action pretrained transformer-based reinforcement learning), a unified framework that enables multiskill locomotion to achieve high-speed traversal in complex environments through autonomous skill transitions using only onboard perception and computation. Our approach generates large-scale, feature-rich two-dimensional (2D) motion datasets through trajectory optimization with simplified dynamics. These datasets enable training of diverse, reusable locomotion skills that transfer effectively to a real quadruped robot operating on complex uneven terrains. The resulting high-quality skills serve as strong priors for efficient learning of complex downstream tasks and extend naturally to 3D environments, enabling smooth, high-speed multiskill locomotion in deployed policy. Real-world experiments demonstrate the framework's capabilities: The robot performed agile maneuvers through complex indoor obstacles and outdoor wild environments, including dynamic drop-down maneuvers that reached instantaneous peak speeds of up to 6 meters per second. A single onboard policy enabled robust traversal of diverse obstacles, including stairs, hurdles, stepping stones, gaps, and fallen branches, demonstrating the versatility and effectiveness of our approach.
The Infinite Sadness of Small Appliances imagines the multiple ways domestic home robots can violate the privacy of a family.
Switchable elements are central to both technological devices and biological machines because they enable controlled and reversible transitions between distinct functional states. Here, we present a DNA origami-based, mechanically bistable snap-through mechanism that can be electrically controlled. This nanoscale switching mechanism exhibits long-term stability in both states in the absence of external stimuli while achieving millisecond-scale switching times upon application of an electric field. Individual devices sustain hundreds of thousands of switching cycles over several hours and remain functional for actuation over several days, offering a powerful platform for systematically studying the endurance and failure mechanisms of biomolecular nanoswitches. As a nanoscale electromechanical interface, our device enables applications in molecular information processing, optical nanodevices, and the dynamic control of chemical reactions. We demonstrate that functionalization with gold nanorods facilitates polarization-dependent optical modulation, establishing direct application in plasmonics. We further show that controlling the accessibility of a molecular binding site allows electrical regulation of reaction kinetics, thereby directly coupling mechanical switching to biochemical function.
An electrically controlled DNA origami snap-through switch provides robust, programmable logic for molecular robotics.
honey bees serve as inspiration for long-range autonomous flight in resource-constrained aerial robot navigation.
HRI should report methods clearly and in full and treat cross-context differences as data, not as replication failures.
After a stroke, individuals often experience mobility impairments because of weakness and loss of independent joint control in the lower limbs. As a result, gait recovery becomes a primary goal of physical rehabilitation, traditionally achieved through high-intensity therapist-led training. However, conventional therapist-led approaches involving manual assistance or resistance can be physically demanding and limit interaction at multiple joints simultaneously. Robotic exoskeletons have emerged as a promising solution, enabling multijoint support, reducing therapist strain, and offering objective performance feedback. However, typical exoskeleton control strategies limit the physical therapist's involvement and adaptability to the patient's needs, which may hinder clinical adoption and outcomes. In this study, we introduce a gait rehabilitation paradigm based on physical human-robot-human interaction that we call therapist-exoskeleton-patient interaction (TEPI), in which a therapist and a patient with stroke are each equipped with a lower-limb exoskeleton virtually connected at the hips and knees via spring-damper elements. This connection enables bidirectional physical interaction, allowing the therapist to guide the patient's movement while receiving real-time haptic feedback. We evaluated this approach with eight patients with chronic stroke using a within-subject design, comparing TEPI training with conventional therapist-guided mobilization during treadmill walking. Results showed that, compared with conventional therapy, TEPI led to greater joint range of motion, increased step length and height, similar muscle activation, and high self-reported motivation and enjoyment. These findings suggest that TEPI can integrate robotic precision with therapist intuition, offering a framework for enhancing gait rehabilitation outcomes in populations recovering from stroke.
Physiological and qualitative data reveal insights into human perceived safety of mobile robot encounters.
Seemingly outdated ultrasound combined with edge-AI denoising can make autonomy more robust when vision fails.
Robotic technologies are expected to drive substantial advancements in planetary exploration and resource prospecting by performing a variety of tasks in extraterrestrial environments. In particular, miniature robots are ideally suited for integration into spacecraft with strict payload limitations, providing a cost-effective solution. However, the pursuit of autonomous exploration using these miniature robots presents challenges owing to constraints in computational power and battery capacity and reduced locomotion performance owing to their small size. Here, we introduce a two-wheeled centimeter-scale rover, designated Lunar Excursion Vehicle 2 (LEV-2), also known as SORA-Q (named after the Japanese words for space and sphere), which transforms into a wheeled configuration from a compact spherical form, enabling efficient traversal of soft lunar terrains. On 19 January 2024 (universal time coordinated), LEV-2 was deployed from a Japanese lunar lander, Smart Lander for Investigating Moon (SLIM), immediately before its landing on the lunar surface. After a lunar landing, the palm-sized rover accomplished autonomous lunar exploration by navigating around the SLIM lander, capturing images of both the SLIM lander and its environment and transmitting selected images through wireless communication on the lunar surface without reliance on ground-based teleoperation. This study details the system design of LEV-2 and presents the results of its in situ lunar activities, highlighting the efficacy of the proposed technologies necessary for mission implementation. Furthermore, we discuss the technical challenges encountered during the mission, including operational constraints and partial data loss, as well as the lessons learned for future exploration missions using small-scale space robots.
Rapid advances in biohybrid microrobots have prompted focused examination of the barriers to their clinical translation.
A neural radiance field-based reconstruction framework merging LiDAR and vision data achieves geometric accuracy.
Lumbar degenerative diseases, primarily caused by pathological tissues compressing spinal nerves, typically necessitate surgical intervention-specifically lumbar nerve decompression-to alleviate pain. Although the anterior decompression approach demonstrates notable advantages, such as reduced bleeding and shorter postoperative hospitalization stays, compared with the conventional posterior approach, patients may still experience incomplete decompression because of various instrumental shortcomings, including restricted visibility and insufficiency of distal dexterity. In this study, we present a robotic surgical system for minimally invasive anterior lumbar nerve decompression, which comprises three slender robotic arms (2 millimeters in outer diameter) with high dexterity (18 degrees of freedom), facilitating effective navigation through the narrow intervertebral disc space to reach the posterior area. Each robot arm is based on concentric push-pull robot structure, forming three robotized instruments: an endoscope for visualization, a laser optical fiber for hemostasis and resection, and a gripper for tissue manipulation. These components are integrated through the hollow lumen of a slender trocar, and multi-instrument coordination enables effective decompression procedure with wide view. System performance was first validated using a three-dimensional-printed vertebral phantom model to confirm accessibility to bilateral articular processes. Subsequently, in vivo animal experiment and human cadaver tests were conducted to further demonstrate the full capabilities in performing minimally invasive lumbar nerve decompression. This study demonstrates the potential of the robotic system to facilitate surgical procedures in narrow, confined, and tortuous anatomical spaces, addressing the key limitations of conventional instruments in anterior lumbar nerve decompression.
Symmetry is a central organizing principle in natural systems, yet its use as a unifying design strategy in robotics has largely remained limited to geometric form. We show that symmetry can instead be leveraged at the level of dynamic actuation capability. We introduce dynamic symmetry, the uniformity of a robot's attainable center-of-mass accelerations, and formalize it through a measure coined as dynamic isotropy. Across more than 1000 simulated morphologies, we found that higher dynamic symmetry consistently improved trajectory tracking, task success, robustness, resiliency, and energy efficiency, with the benefits becoming most pronounced as dynamic isotropy approached its theoretical limit. To study this regime systematically, we developed Argus, a family of spherical robots designed to explore the effects of increasing dynamic symmetry. Members of the Argus family vary in their actuation geometry and dynamic symmetry level while sharing a common architectural principle: radially oriented linear actuators that directly shape the robot's center-of-mass dynamics. Among them, we built a physical 20-leg Argus variant that achieved near-extreme dynamic isotropy and demonstrated orientation-invariant locomotion, agile traversal of cluttered and deformable terrain, rapid self-stabilization, and resilience to partial actuator failures. Its distributed sensing further enabled omnidirectional perception and object interaction during continuous motion. These results show that designing robots for symmetry not only in morphology but also in their attainable dynamics provides a powerful and general pathway toward agility, robustness, and multifunctionality in uncertain terrestrial and extraterrestrial environments.
The validity of virtually represented robots in HRI experiments depends on when, where, and for whom it matters.
Four science fiction works describe realistic construction and mining robots enabling human habitation of the Moon.
