Full-sized humanoid robot capabilities have grown exponentially in recent years, aiming towards general-purpose deployment in human environments. A popular control method used by manufacturers utilizes Virtual Reality for upper-body teleoperation and Reinforcement Learning for lower-body balance and locomotion control. As a result, a single remote operator can see, manipulate, and navigate about a real, distant physical environment. This powerful control stack is often relegated to expensive full-sized robots, many of which are inaccessible to the research community. Miniature humanoids are more prevalent, but employ less biomimicry in their design (e.g. fewer sensors, Degrees of Freedom, etc) and lack similar developments. This paper describes a compliant full-body telepresence control stack developed from the ground up for miniature humanoids. Framework experimentation on ROBOTIS OP3 hardware showcases walking at speeds up to 0.45 m/s independent of arm motions. Tele-loco-manipulation is demonstrated via a cube relocation experiment with an expert human operator. On average, the teleoperated system moved 2 different 40 g cubes within 10 mins, walking a total distance of 5 m. Overall, the developed system shows potential for miniature humanoid tele-loco-manipulation.
Decades of studies in psychology and neuroscience establish that humans naturally synchronize their movements with others. This phenomenon is not limited to Human-Human Interaction (HHI) but also observed in Human-Robot Interaction (HRI), where individuals align their motions to the rhythm of a robot without conscious effort. While such influences are often subtle, characterizing the dynamics of robot-to-human synchronization is critical for designing effective collaborative systems. This study explores whether humans synchronize differently to the movements of robots compared to other humans, and at what point individuals become conscious of the changes in the robot's movements. Our results reveal an asymmetry in synchronization between HHI and HRI. We find that participants are more likely to notice changes in a robot's speed, particularly when the robot speeds up or slows down by over 20% compared to the base speed. Participants' perception of the robot's animacy is also influenced by these speed changes. Building on our findings, we provide insights into how roboticists can design robot behaviours to minimize unwanted influence and respect human autonomy.
This work introduces a controller for an upperlimb rehabilitative exoskeleton based on reservoir computing (RC). The controller decodes the motor intention of the user by observing the electromyographic (EMG) activity of four upperlimb muscles and end effector (EE) kinematics and then assists the movements of upper-limb during the execution of planar reaching tasks. After tuning the hyperparameters of the RC, the controller was tested by three healthy participants wearing a shoulder-elbow active exoskeleton. The controller predicted the direction of reaching movements across eight possible targets positioned on a 25 cm circumference, achieving an average accuracy of $\mathbf{7 4. 1 2 \%}$. Given the geometric structure of the task, we introduced a macro-direction measure of goodness (MDG) metric that considered both correct predictions and those corresponding to targets adjacent to the true one, resulting in an average performance of 96.63 %. Moreover, RC-ID outperformed a kinematics-only benchmark before kinematic onset and surpassed an EMG-only benchmark during the later phases of the reaching movement execution. Finally, effects of assistance were assessed by evaluating the variation of muscular activation during exoskeleton-assisted movements, which led to reductions up to $-47.4 \%$ with respect the activations during unassisted movements.
Lower-limb exoskeletons can restore walking ability for individuals with mobility impairments by augmenting human strength. However, even millimeter-scale obstacles or minor surface irregularities can adversely affect an exoskeleton's stability and hinder real-world deployment. This paper presents XoPercept, an integrated 360° perception system for the selfbalancing XoMotion exoskeleton that employs multiple RGB-D cameras to continuously map the walking surface. In indoor experiments, XoPercept detected $\mathbf{9 8 \%}$ of obstacles as small as 1 mm and, using a novel hybrid sizing method, provided 3D measurements of obstacles with errors under 3 cm, thereby generating detailed terrain models. By closing this critical safety gap, XoPercept enhances the reliability and confidence of exoskeleton-assisted mobility in everyday environments.
The functions demanded of a robot vary with the environment. For a transformable robot that can switch among multiple configurations, the ability to adopt a wheeled mode—which yields highly efficient and stable locomotion—is crucial. The locations where wheels are mounted strongly influence both the robot's performance in wheel mode and the range of environments it can handle. Although wheel placement dictates the wheel-mode posture, that posture must remain reachable from the legged mode through a feasible transformation. This paper proposes a method for selecting wheel placements that enable transformation between a bipedal mode and tri-swerve mode, together with an ankle joint design that realizes those placements. First, we establish a systematic taxonomy of wheel configurations and, based on this evaluation, demonstrate the advantages of a steering ankle joint. Second, to achieve both leg-wheel transformation and steered wheeled locomotion, each ankle incorporates a wrap-free wire differential that provides a wide workspace. Experiments show that the proposed robot exploits the pitch and roll axes of its wire-differential steering ankles to transform smoothly and to perform both bipedal and tri-swerve motion.
