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.
We present ANUBIS—a compact, compliant, and affordable humanoid mobile manipulation robot designed primarily for safe interaction and versatile task execution in everyday household environments. The platform integrates two lightweight, compliant 6 degree-of-freedom (DOF) arms onto a compact, cylinder-shaped omnidirectional mobile base, enabling agile navigation through standard doorways and tight indoor spaces. Torque-controlled quasi-direct-drive (QDD) actuators and a layered impedance control framework ensure inherent compliance, facilitating safe physical interactions with humans and household objects alike. Additionally, extensive use of off-the-shelf components and 3D-printed structures contributes to cost efficiency, keeping the total bill-of-materials near USD 11,200. Real-world experiments demonstrated autonomous vision-based manipulation and intuitive real-time bimanual teleoperation, validating the robot's practical safety and versatility.
Accurately predicting where a person will place their foot during walking has practical value in applications that require close coordination between humans and machines, such as exoskeletons that adapt to a user's movement, or systems that detect and prevent trips and falls in real-world environments. Current methods often rely on complex models or offline analysis. In this paper, we present the use of probabilistic movement primitives (ProMPs) for predicting user step lengths in real time during walking on a treadmill. We used kinematic data acquired with an inertial measurement system to mimic data potentially gatherable from a wearable exoskeleton, avoiding the need for external motion capture. We evaluated the method with nine subjects walking on a treadmill. We show accurate prediction of user step length as early as 100 ms after movement onset during the foot's swing phase. This method could be extended and integrated with environmental monitoring systems to predict potential foot-obstacle collisions in real time.
Robotic hands often struggle to replicate the dexterity and stability of human grasping, particularly in tasks requiring precise thumb opposition or passive compliance. Although most designs approximate the complex trapeziometacarpal (TMC) joint with simplified 2-degree-offreedom mechanisms, they rarely optimize axis placement for anatomical fidelity or evaluate opposition with orientationaware metrics. Likewise, soft tissue structures like the thenar eminence (TE) and first web space (FWS) are frequently omitted or included only cosmetically, limiting ergonomic grip quality. Here, we introduce a design and evaluation workflow for anatomical thumb replication in robotic hands. Using a compact parameter space, a MATLAB framework systematically optimizes TMC joint geometry for human-like motion. To evaluate grasping performance, we propose the Kapandji-plus test, which augments standard reachability assessments with orientation scoring across 17 functional poses. Additionally, we developed 21 novel passive TE and FWS prototypes with varied compliance and geometry, tested using task-oriented quantitative and qualitative metrics. Results show that two optimized joint configurations significantly improve thumb orientation accuracy and Kapandji-plus scores over a baseline configuration. The best-performing TE and FWS designs enhance power and precision grips, with a kirigami-inspired FWS demonstrating repeatable lateral support in manipulation tasks. These components advance anthropomorphic hand design by combining anatomically informed mechanics with task-oriented validation.
This study proposes a robust and generalised contact detection framework for bipedal robots based on foot IMUs. The framework employs a hierarchical fuzzy inference approach to identify contact with the ground and sliding. The proposed approach integrates a general type-2 fuzzy clustering mean for contact detection with an interval type-2 fuzzy logic system for slide detection. This integration enables the model to handle multidimensional uncertainty and generalise across a wide range of terrain and gait conditions. A further advantage of the foot-mounted IMU approach is that it eliminates the need for external contact sensors or force transducers. In comparison to black-box deep learning approaches, this approach offers enhanced interpretability and generalization capabilities. The efficacy and versatility of the framework are demonstrated by simulation experiments, which show an average contact detection accuracy exceeding 95% in both structured and unstructured terrain scenarios without external contact force sensors or large datasets for the required training.
State of the art robotic systems still struggle to accomplish complex tasks, such as bimanual manipulation, in unpredictable environments due to limitations in sensing, adaptability, and control - leading to artificially restricted autonomous system performance. This research addresses this limitation by introducing a novel metric for quantifying antagonistic internal forces during teleoperated bimanual manipulation, conveyed through minimalistic vibrotactile feedback (VF) to the operator. Experimental tests demonstrated that integrating the proposed metric through VF improved internal force regulation, reducing average internal forces by approximately 39.5% and decreasing force variability by 52.1% compared to trials without feedback. Introducing VF exhibited an increased number of occurrences of payload drops - indicative of operators pushing closer to operational limits. These findings confirm the proposed metric delivered through minimalistic VF substantially enhances teleoperated manipulation, providing a promising avenue for safer, more intuitive robotic operations in dynamic and uncertain environments.
As robots become increasingly embedded in human environments, the ability to anticipate the outcomes of physical contact is crucial for enabling safe, adaptive, and socially intelligent behavior. Thus, learning to discriminate harmful sensory patterns from the benign ones will not only ensure physical safety during robot interaction, but may also lay the foundation for artificial empathy through mirroring the pain of others. To this end, this work develops a framework for tactile prediction through multimodal learning, emphasizing the integration of visual and tactile information in a common latent space. The ability to predict tactile sensations prior to contact allows a robot to avoid harmful outcomes as well as internalizing the tactile experience of others. We adapt the Deep Modality Blending Network (DMBN) as a foundational model for this task. Using demonstrations involving both gentle and noxious human touch, synchronized visual and tactile data are collected to train the model. After learning, the robot can generate temporal tactile activations from visual observations alone, anticipating sensory outcomes before physical contact occurs. Experiments on an upper-body humanoid robot show that it can predict painful stimuli and mirror tactile experiences observed in others. The key contributions of this study include: (1) the development of a predictive tactile perception framework using DMBNs, (2) the adaptation of this framework for modeling artificial pain that may be used as a basis for artificial empathy, and (3) empirical validation using real-world humanrobot interaction scenarios.
Humans naturally coordinate multiple fingers to perform a wide range of daily tasks in an ergonomically efficient manner. In contrast, robotic hands have yet to fully leverage such dexterity, often executing uncoordinated actions despite their multi-fingered structures. To bridge this gap, we introduce the Synergetic Dexterity Challenge: How can robotic hands effectively synergize their fingers to enable intuitive and efficient object manipulation, akin to human-level dexterity? To address this challenge, we propose SynPoldex, a novel bi-level pipeline that integrates high-level planning with low-level reinforcement learning to achieve coordinated finger control. Specifically, our method consists of two components. First, we employ an explicit chain-of-thought approach using a vision-language model for multi-turn reasoning and reflection, which generates a taskspecific finger synergy plan by categorizing fingers into either dominant or non-dominant roles. Second, we acquire a lowlevel policy to execute motions based on the synergy plan. To incorporate the finger plan as prior knowledge, we reshape the reward function to encourage appropriate contact, and applies an advantage factorization technique to enhance policy learning. We evaluate SYNPOLDEX across a variety of tasks and three different robot embodiments. Experimental results show that Synpoldex achieves higher success rates, faster convergence, and higher human-likeness score compared to baseline methods.
Industrial automation increasingly demands flexible and robust grasping solutions for objects of varied geometries and sizes. Conventional parallel-jaw grippers frequently struggle to achieve stable contact in complex industrial contexts, causing inefficiencies and downtime. To overcome such challenges, adaptive grippers characterized by multi-axis control and multi-contact capabilities are a commercially viable alternative. However, traditional volumetric grasping techniques catering to parallel-jaw grippers are not directly applicable due to the difference in contact mechanics and actuation constraints, necessitating the development of novel grasping algorithms for enabling flexible multi-point contact. In this paper, we propose a framework for learning volumetric grasp generation for large industrial objects using a sophisticated adaptive gripper comprising of eight independently movable axes and four suction grippers. We develop a novel approach for generating grasp candidates by using inverse kinematics to search for suitable gripper configurations that align the point clouds of both the adaptive gripper and the target object surface to capture critical geometric features, yielding a robust set of multi-contact training examples. We then propose a neural rendering-based volumetric grasp detection approach to predict suitable grasp candidates for a multi-axis gripper based on the global and local geometric information of each end-effector. Our experiments validate the efficacy of our proposed grasp generation approach that achieves a promising grasp success rate of $\sim 95 \%$ with different industrial objects. This highlights the effectiveness of combining adaptive multi-contact strategies with geometry-centric data generation.
This paper presents ARTEMIS, an full-sized humanoid robot designed for dynamic motions. With 20 active degrees of freedom using custom proprioceptive actuators, ARTEMIS is capable of walking up to $2.1 ~\mathrm{m} / \mathrm{s}$ using a model-based control approach, making it one of the fastest humanoid robots at the time. It can also seamlessly transition between walking and running, making it the first platform entirely developed in academia to demonstrate such capabilities. This paper explains the details of the platform as well as the controller. ARTEMIS's performance and robustness are validated on various outdoor terrains as well as by winning a global robotics soccer competition. Having validated the platform, we open-source it to the wider community, starting from its actuation approach to the robot model with baseline controllers, to provide an accessible foundation for making custom humanoids.
We present Cascaded Predictive Control (CPC), a framework that decomposes a model predictive control horizon into multiple stages, each solved with the model and method best suited to its accuracy requirements. CPC transfers information across stages through the value function: the terminal cost produced by a downstream stage acts as a boundary condition for the preceding one, enabling heterogeneous combinations of different techniques and solvers. Two proof-of-concept studies illustrate the approach. First, a humanoid walking controller uses a double DDP cascade that schedules single rigid body and linear inverted pendulum models to extend the prediction horizon at a lower computational cost. Second, a cart-pendulum swing-up couples a DDP stage with a value function learned by an actor-critic policy; the hybrid controller succeeds where the policy alone fails. Although the examples are preliminary, they indicate that CPC can provide a flexible framework for achieving effective results with reduced computational effort by combining the strengths of different techniques.
Wearable sensors enable accurate estimation of joint moments through easy-to-use myography-based methods, such as force myography (FMG), offering practical benefits and valuable insights into continuous muscle state estimation to enhance control strategies. This paper presents a comparative analysis of four commonly used machine learning methods, Gaussian process regression (GPR), support vector regression (SVR), feed-forward neural network (FFNN), and temporal convolutional network (TCN), for estimation of human knee and ankle joint torques based on joint angles, velocities, and FMG signals from eight muscles on the human leg. The performance of the methods was evaluated on isokinetic motions of ten participants and compared to the models enhanced by electromyography (EMG) signals. Among the evaluated models, neural networks consistently demonstrated the highest accuracy in both inter- and intra-participant validations. Incorporating FMG modality yielded comparable performance to EMGbased estimation for unknown participants. Additionally, FMG outperforms EMG-based estimation in novel task characteristics within a single participant. These findings demonstrate the potential of FMG as a viable alternative to EMG for human joint torque estimation and highlight its potential for personalized exoskeleton control.
This paper presents GD-Prox, a Haptic-Glove Dual-Proxy framework for remote dexterous robot hand telemanipulation. The proposed method extends the Dual-Proxy approach to glove-hand systems, addressing inherent actuation constraints and enabling stable bilateral interaction over lowbandwidth networks without direct task-space force transmission. Independent local controllers on the glove and robothand sides maintain responsive behavior under significant communication delays and reduced update rates. Experimental validation using intermittent press-release and sustained pinch-grasp tasks confirms the effectiveness of the approach in maintaining stable bilateral coordination under latency. The framework establishes a robust foundation for teleoperating robot hands with limited degrees of freedom, paving the way for future extensions to inter-finger coordination and dynamic object manipulation.
Flexure-based mechanisms offer a promising alternative to traditional rigid-link joints in robotic hands. They enable simplified construction, reduced weight, and adaptability through their intrinsic compliance. This paper presents the design and mechanical characterization of a compound flexure-hinge joint with a lattice membrane for use in a monolithic anthropomorphic hand. A series of experimental tests were conducted to evaluate the joint's stiffness in flexion and torsion, which were compared to joints from existing literature. Additionally, its durability under cyclic loading was examined, together with the functional grasping capabilities of a full anthropomorphic hand utilizing this joint. Results demonstrate that the joint exhibits increased torsional stiffness of $\mathbf{5. 5 1} \boldsymbol{\pm} \mathbf{0. 4 7 ~ N m m} /$ degree compared to $\mathbf{0. 5 2} \pm 0.08 \text{Nmm} /$ degree for a simple flexure-hinge joint. Additionally, it has a smooth response under flexion at a stiffness of $0.50 \pm 0.07 ~\mathrm{N} / \text{mm}$ compared to a full membrane which has a variable stiffness between $0.54 \pm 0.10 ~\mathrm{N} / \text{mm}$ and $1.30 \pm 0.17 ~\mathrm{N} / \text{mm}$, caused by a buckling response. Preliminary results also showed that the joint can withstand cyclic loading without failure, but with some deformation, for over $\mathbf{3 0 0, 0 0 0}$ cycles, and the ability of a hand utilizing these joints to perform essential grips for successful operation. Clear benefits to incorporating the lattice structure are presented, which include, added torsional stiffness compared to a simple flexurehinge joint, a smooth force profile compared to a full membrane. The lattice design also prevents object interference with the joint cavity during grasping. These findings support the viability of the proposed design approach for use in adaptive, lightweight and compact robotic hands, with applications in humanoid robots and upper-limb prosthetics.
Despite the promise of elastic actuation for energy efficiency and performance, the deployment of robotic systems with elastic joints capable of executing highly dynamic and explosive motions is still limited. We address that gap in this paper by advancing the Bi-Stiffness Actuation (BSA) concept through the design and control of a fully integrated BSA joint (iBSA) that combines elastic energy storage with robust high-speed control. Particularly, we address the need for system-level integration rather than isolated component analysis, as commonly seen in existing literature. Additionally, we introduce an advanced and robust high-speed control system specifically tailored to the iBSA joint's complex control and dynamic response requirements. Through detailed simulations and experimental validation, we demonstrate that the iBSA joint significantly improves efficient energy transfers, precise control, and dynamic performance in challenging tasks such as explosive movements. We develop the iBSA joint with a focus on optimising both actuator design and overall system performance. Moreover, this work addresses key challenges in the timing control of energy storage and release, enabling a practical framework for implementing next-generation high-performance elastic manipulators.
Over the years, robotic manipulation has primarily focused on end-effectors, an approach that severely limits a robot's ability to manipulate large and heavy objects. Humanoids, which are expected to operate in human environments, must acquire this skill to enhance their versatility and usefulness. In this regard, we present a whole-body multi-contact manipulation (WBMC) framework to handle large and heavy objects. To facilitate WBMC, we incorporate artificial skin patches distributed across the humanoid's upper body which are effectively utilized for contact detection and force sensing. The WBMC manipulation problem is formulated as an optimal control problem (OCP) within a model predictive control (MPC) framework, and three different types of dynamic motions are used to evaluate the controller's effectiveness. The proposed framework manages the entire manipulation process, including reaching, grasping, picking up, and manipulating. Furthermore, these motions are leveraged to develop a two-stage object inertial parameter estimation framework. The first stage estimates the object's mass and center of mass, while the second estimates its inertia. Both the manipulation and estimation frameworks are numerically evaluated using the TALOS humanoid and a rectangular box in simulation, and their respective results are presented and discussed.
