Control policies are often implemented with fixed-capacity multilayer perceptrons trained by backpropagation, which require architecture selection in advance and cannot adapt their capacity during learning. This paper introduces the Self-Motivated Growing Neural Network (SMGrNN), a gradient-trained controller whose topology evolves online through a local Structural Plasticity Module (SPM). The SPM monitors edge-wise weight update statistics over short temporal windows and uses these local signals to trigger neuron insertion and pruning, while synaptic weights are optimized by a standard gradient-based optimizer. This allows network capacity to be adjusted during learning without manual architectural tuning. SMGrNN is evaluated on control benchmarks via policy distillation. Compared with multilayer perceptron baselines, it achieves similar or higher returns, lower variance, and task-appropriate network sizes. Ablation studies with growth disabled and growth-only variants isolate the role of structural plasticity, showing that adaptive growth improves reward stability while pruning prevents uncontrolled expansion and supports compact network formation. These results establish the independent value of local structural plasticity within gradient-trained networks and motivate future investigation of whether similar structural rules can be extended to more local or spike-based learning settings.

In practical robotic applications, most tasks require a combination of large-range motion and fine operation. Effectively coordinating these two aspects poses a significant challenge due to the mismatch between the limited workspace of the leader robot and the expansive workspace of the follower manipulator. To address this issue, we propose a dual-scale teleoperation framework that enables the seamless integration of large-range movement and fine operation. Furthermore, we design an adaptive force feedback strategy based on fuzzy logic, which dynamically allocates environmental contact and constraint forces between the dual leaders with different mapping scales, ensuring closed-loop control of the system. A comprehensive analysis of the framework's stability and transparency is provided. Finally, the proposed force feedback teleoperation framework is experimentally validated against existing methods. In the fixed-point alignment task, the proposed method achieves a success rate of 85.7% and reduces completion time by approximately 10%. In the narrow passage navigation task, the manipulator successfully passes through a 120 mm-wide channel (with a manipulator width of 115 mm), while significantly reducing the number of collisions. These experimental results demonstrate the effectiveness of the proposed approach in achieving fine operations within large workspaces.

Human machine teaming (HMT) applications, bolstered by advancements in control technology, are proliferating across industries. Despite this, accurately predicting HMT performance and time efficiency, particularly in open and potentially hazardous environments, remains challenging due to the complexity of modeling mission difficulty and human behavior. This study tackles the challenge by extending cursor-based Fitts' law into a 3-D context with six degrees of freedom. This represents the first attempt to model task difficulty in HMT by integrating both target translational and orientational components, effectively expanding Fitts' law beyond human–computer interaction to robots and machinery. This novel approach is tailored for HMT applications and tested through an excavator simulation and a quadruped manipulator robot experiment. The study showcases our model's capability to predict the performance of complex, multistep HMT tasks accurately. This research opens a novel pathway in HMT performance prediction, offering promising implications for real-world applications.

This article presents a standardized human–robot teleoperation interface (HRTI) evaluation scheme for mobile manipulators. Teleoperation remains the predominant control type for mobile manipulators in open environments, particularly for quadruped manipulators. However, mobile manipulators, especially quadruped manipulators, are relatively novel systems to be implemented in the industry compared to traditional machinery. Consequently, no standardized interface evaluation method has been established for them. The proposed scheme is the first of its kind in evaluating mobile manipulator teleoperation. It comprises a set of robot motion tests, objective measures, subjective measures, and a prediction model to provide a comprehensive evaluation. The motion tests encompass locomotion, manipulation, and a combined test. The duration for each trial is collected as the response variable in the objective measure. Statistical tools, including mean value, standard deviation, and T-test, are utilized to cross-compare between different predictor variables. Based on an extended Fitts' law, the prediction model employs the time and mission difficulty index to forecast system performance in future missions. The subjective measures utilize the NASA-task load index and the system usability scale to assess workload and usability. Finally, the proposed scheme is implemented on a real-world quadruped manipulator with two widely-used HRTIs, the gamepad and the wearable motion capture system.

When experiencing disturbances during locomotion, human beings use several strategies to maintain balance, e.g. changing posture, modulating step frequency and location. However, when it comes to the gait generation for humanoid robots, modifying step time or body posture in real time introduces nonlinearities in the walking dynamics, thus increases the complexity of the planning. In this paper, we propose a two-layer hierarchical optimization framework to address this issue and provide the humanoids with the abilities of step time and step location adjustment, Center of Mass (CoM) height variation and angular momentum adaptation. In the first layer, times and locations of consecutive two steps are modulated online based on the current CoM state using the Linear Inverted Pendulum Model. By introducing new optimization variables to substitute the hyperbolic functions of step time, the derivatives of the objective function and feasibility constraints are analytically derived, thus reduces the computational cost. Then, taking the generated horizontal CoM trajectory, step times and step locations as inputs, CoM height and angular momentum changes are optimized by the second layer nonlinear model predictive control. This whole procedure will be repeated until the termination condition is met. The improved recovery capability under external disturbances is validated in simulation studies.

Bipedal robots have gained a lot of locomotion capabilities the past few years, especially in the control level. Navigation over complex and unstructured environments using exteroceptive perception, is still an active research topic. In this paper, we present a footstep planning system to produce foothold placements, using visual perception and proper environment modeling, given a black box walking controller. In particular, we extend a state-of-the-art search-based planning approach (ARA*) that produces 6DoF footstep sequences in 3D space for flat uneven terrain, to also handle rough curved surfaces, e.g. rocks. This is achieved by integrating both a curved patch modeling system for rough local terrain surfaces and a flat foothold contact analysis based on visual range input data, into the existing planning framework. The system is experimentally validated using real-world point clouds, while rough terrain stepping demonstrations are presented on the WALK-MAN humanoid robot, in simulation.

This paper introduces the design of a novel under-actuated hand with highly integrated modular finger units, which can be easily reconfigured in terms of finger arrangement and number to account for the manipulation needs of different applications. Each finger module is powered by a single actuator through an under-actuated transmission and equipped with a sensory system for delicate and precise grasping, which includes absolute position measurements, contact pressure sensing at finger phalanxes and motor current readings. Finally, intrinsic elasticity integrated in the transmission system make the hand robust and adaptive to impacts when interacting with the objects and environment. This highly integrated hand (HERI II) was developed for the Centauro Robot to enable robust and resilient manipulation. A set of experiments demonstrating the hand's grasping performance were carried out and fully verified the design effectiveness of the proposed hand.

To generate dynamic motions such as hopping and running on legged robots, model-based approaches are usually used to embed the well studied spring-loaded inverted pendulum (SLIP) model into the whole-body robot. In producing controlled SLIP-like behaviors, existing methods either suffer from online incompatibility or resort to classical interpolations based on lookup tables. Alternatively, this paper presents the application of a data-driven approach which obviates the need for solving the inverse of the running return map online. Specifically, a deep neural network is trained offline with a large amount of simulation data based on the SLIP model to learn its dynamics. The trained network is applied online to generate reference foot placements for the humanoid robot. The references are then mapped to the whole-body model through a QP-based inverse dynamics controller. Simulation experiments on the WALK-MAN robot are conducted to evaluate the effectiveness of the proposed approach in generating bio-inspired and robust running motions.

This paper offers a novel insight of using nonlinear models for the control to produce more robust and natural walking gaits for humanoid robots. The sagittal and lateral gait control needs to be treated differently, hence, we proposed two types of suitable nonlinear models, which allow forward simulations to look ahead, and thus, predict accurately the future trajectory/state at the end of the current step. Subsequently, by performing multiple forward simulations in a similar manner for the next step and using the gradient descent method, an appropriate foot placement can be found to achieve precise walking speed. By doing this two-step lookahead, all trajectories of the support and the swing leg can be generated. Our proposed controller can plan trajectories at the beginning of each step or actively re-plan according to task state errors. It is validated effectively in simulations performed in both ADAMS and Open Dynamic Engine. The robot can successfully traverse up/down a stair and recover from pushes with more natural looking gaits compared to the conventional bent-knee style. The reasonable computational time also indicates the feasibility of real-time implementation on real robots.

In this paper, the Hardware Embedded Reduced Intricacy (HERI) Hand, which is a novel tendon driven three-finger under-actuated hand demonstrating balanced dexterous finger manipulation and powerful grasping of common objects is presented. The third finger of HERI Hand is asymmetrically designed in terms of dimensions to emulate the functionality for combining middle finger, ring finger and little finger of a human hand. HERI Hand is equipped with three actuators devoted to the actuation of the flexion of the index finger and thumb, the flexion of the third finger and finally the thumb abduction and adduction motion with the latest drive having no interference with other transmissions. The proposed hand is capable of realizing delicate finger manipulation such as opening a lidded cup, which is super suitable to accomplish in such configuration. At the same time the hand demonstrates high grasping strength capacity thanks to the actuation sizing permitted by the under-actuated configuration. The hand is also equipped with tactile/pressure sensors distributed in the phalanxes, which leaves open the possibility of potential applications for sophisticated finger manipulations taking into account the phalanxes contact forces. Three different sets of experiments were carried out to demonstrate the performance of HERI Hand and validated its functionality.

This paper is motivated by the questions: What would happen if a humanoid robot is put on a Segway? Is it possible for the humanoid robot to use this transportation device that is specifically designed for human? Simulation involving a two-wheeled mobile platform (TWMP) and our humanoid robot COMAN (COmpliant HuMANoid Platform) shows that it is indeed feasible without any hardware modification. Regarding the implementation, the full dynamics of the humanoid robot is considered and quadratic optimization is employed to generate whole-body joint torques to realise two types of tasks according to the interaction type between the TWMP and the humanoid robot. The TWMP is considered as unknown disturbance and the humanoid robot has to keep balancing on it in the first type of task. On the contrary, the active movement of the humanoid robot is utilised as an interface to intuitively drive the TWMP in the second type of task. For both tasks, tracking the position of center of mass (CoM) and regulating the angular momentum around it are considered as primary objectives, stabilizing the posture of certain part of its body is optional. In addition, both tasks are repeated on uneven terrain to demonstrate the robustness of the control method.

A novel control synthesis is proposed for humanoids to demonstrate unique foot-tilting behaviors that are comparable to humans in balance recovery. Our study of model-based behaviors explains the underlying mechanism and the significance of foot tilting well. Our main algorithms are composed of impedance control at the center of mass, virtual stoppers that prevent overtilting of the feet, and postural control for the torso. The proof of concept focuses on the sagittal scenario and the proposed control is effective to produce human-like balancing behaviors characterized by active foot tilting. The successful replication of this behavior on a real humanoid proves the feasibility of deliberately controlled underactuation. The experimental validation was rigorously performed, and the data from the submodules and the entire control were presented and analyzed.

In this paper, we propose an Energy based Fall Prediction (EFP) which observes the real-time balance status of a humanoid robot during standing. The EFP provides an analytic and quantitative measure of the level of balance. Both simulation and experimental studies were conducted and compared with the previously proposed indicators, such as Capture Point (CP) and Foot Rotation Indicator (FRI). The EFP also suggests the balance augmentation by active foot tilting to create larger potential barriers. As a proof of concept, a hybrid balance controller was designed to stabilize the robot including under-actuation phases so the robot can also balance with shoes. Our study reveals that both EFP and CP successfully predict falling about 0.2s in advance for the tested robot, while the FRI fails due to the light weight of the foot and limited resolution of the force/torque measurement.

The work presents the development and evaluation of a novel foot sole for humanoid robots. For humanoid locomotion the foot sole is important for absorbing impacts. In contrast to the simple planar rubber pad foot sole that is conventionally used in humanoid robots this paper introduces a new foot sole design in which the dissipation of energy during collision is done effectively using a viscous air damping sole mechanism that provides better reduction of the ground impact forces. The paper describes the principle of the foot sole and provides details of its design and implementation. Experimental trials were performed with the child size humanoid robot, COMAN, wearing the proposed feet to validate their performance during landing and walking. The results demonstrate that the proposed new passive damping mechanism can reduce effectively the ground reaction impact forces and oscillations while maintaining the foot/body posture.

We propose a novel control framework to demonstrate a unique foot tilting maneuver based on ankle torque control for humanoid balance recovery. The framework consists of the variable impedance regulation at the center of mass of the robot based on the ankle torque control, the virtual stoppers to prevent over tilting of the feet, and the body attitude control. The scope of our paper focuses on the sagittal scenario as the first proof of concept on the balance recovery by means of active foot tilting without losing stability. Our study demonstrates the success of the control implementation for the humanoid push recovery and the feasibility of having actively controlled foot tilting. The experimental data are presented and analyzed.

In this paper, we resolve the issue of stepping over a large obstacle by exploiting the redundancy of pelvis rotation and the versatility of foot trajectories for the humanoids. The control framework consists of a motion pattern that exploits the redundancy of pelvis rotation to enlarge the kinematic workspace, a generic foot trajectory generation which can be modified by a parametric interface to adapt to a specific task as well as utilizing the hip abduction to avoid obstacle collision. Moreover, the compensation strategies are also presented for reducing the discrepancies to implement the dynamic stepping motion on a real robot. The effectiveness is validated by COMAN's capability of dynamically stepping over a large obstacle of 10cm height by 5cm width which is almost 20% of its leg length in both simulation and experiment.

This paper presents a passivity based compliance stabilizer for humanoid robots. The proposed stabilizer is an admittance controller that uses the force/torque sensing in feet to actively regulate the compliance for the position controlled system. The low stiffness provided by the stabilizer permits compliant interaction with external forces, and the active damping control guarantees the passivity by dissipating the excessive energy delivered by disturbances. Both the theoretical work and simulation validations are presented. The effectiveness of the stabilizer is demonstrated by the simulations of a simplified cart-table model and the multi-body model of a humanoid under impulsive/periodic force perturbations during standing and walking in place. Simulation data show the quantitative evaluation of the stabilization effect by comparing the responses of body attitude, center of mass, center of pressure without and with the stabilizer.