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To achieve rapid balance recovery of humanoid robots under external shocks, a bionic bipedal balance control approach based on control priority and target attitude adjustment was developed. In order to successfully restore equilibrium, the system first automatically modifies the control priority based on the impact magnitude and stability margin. This means that when the center of mass approaches the support border, it receives a higher attitude control priority. Second, the discriminating mechanism for the shock confrontation and balance recovery stages was designed, and the target attitude of the robot’s centroid of mass was modified appropriately for each stage. The optimum three-dimensional force at the foot end was then obtained by combining MPC’s state prediction in the future time domain with a quadratic programming solution, allowing the robot to quickly restore its equilibrium after being disturbed by the external environment. External force striking test were conducted on flat and irregular ground while the robot was standing bipedally. The results suggest that the proposed strategy can improve the robot’s ability to withstand stronger external shocks and swiftly recover balance following external hits.

Much of the allocation of human resources to tasks is studied under the rubric of "attention". However this is a very low-dimensional characterization of a system that has many degrees of freedom. To make progess in understanding human brain resource allocations, we will need to understand its basic functions at an abstract level. One way of accomplishing such an integration is to create a model of a human that has a useful amount of complexity. Essentially, one is faced with proposing an embodied "operating system" model that can be tested against human performance. Recently, technological advances have been made that allow progress in this direction. Graphic models that simulate extensive human capabilities can be used as platforms to develop synthetic models of visuo-motor behavior. Currently, such models can capture only a small portion of a full behavioral repertoire, but for the behaviors that they do model, they can describe complete visuo-motor subsystems at a level of detail that can be tested against human performance in realistic environments. This paper outlines one such model and shows both that it can produce interesting new hypotheses as to the role of vision and also that it can greatly enhance our understanding of a more multifacted characterization attention in visuo-motor tasks.

In this paper we describe an autonomous strategy which enables a humanoid robot to learn how to reach for a visually identified object in the 3D space. The robot is a 22-DOF upper-body humanoid with moving eyes, neck, arm and hand. The robot is bootstrapped with limited a-priori knowledge, sufficient to start the interaction with the environment; this interaction allows the robot to learn different sensorimotor mappings, required for reaching. The arm-head forward kinematic model and a visuo-motor inverse model are learned from sensory experience. Learning is performed purely online (without any separation between training and execution) through a goal-directed exploration of the environment. During the learning the robot is also able to build an internal representation of its reachable space.

Compliance control is highly relevant to human safety in human–robot interaction (HRI). This paper presents a review of various compliance control techniques. The paper is aimed to provide a good background knowledge for new researchers and highlight the current hot issues in compliance control research. Active compliance, passive compliance, adaptive and reinforcement learning-based compliance control techniques are discussed. This paper provides a comprehensive literature survey of compliance control keeping in view physical human robot interaction (pHRI) e.g., passing an object, such as a cup, between a human and a robot. Compliance control may eventually provide an immediate and effective layer of safety by avoiding pushing, pulling or clamping in pHRI. Emerging areas such as soft robotics, which exploit the deformability of biomaterial as well as hybrid approaches which combine active and passive compliance are also highlighted.

Compliance control is highly relevant to human safety in human–robot interaction (HRI). This paper presents multi-dimensional compliance control of a humanoid robot arm. A dynamic model-free adaptive controller with an anti-windup compensator is implemented on four degrees of freedom (DOF) of a humanoid robot arm. The paper is aimed to compliment the associated review paper on compliance control. This is a model reference adaptive compliance scheme which employs end-effector forces (measured via joint torque sensors) as a feedback. The robot's body-own torques are separated from external torques via a simple but effective algorithm. In addition, an experiment of physical human robot interaction is conducted employing the above mentioned adaptive compliance control along with a speech interface. The experiment is focused on passing an object (a cup) between a human and a robot. Compliance is providing an immediate layer of safety for this HRI scenario by avoiding pushing, pulling or clamping and minimizing the effect of collisions with the environment.

In this paper, a control system for humanoid robot walking is approximately optimized by means of reinforcement learning. Given is a 18 DOF humanoid whose gait is based on replaying a simple trajectory. This trajectory is translated into a reactive policy. A neural network whose input represents the robot state learns to produce appropriate output that additively modifies the initial control. The learning algorithm applied is actor–critic with experience replay. In 50 min of learning, the slow initial gait changes to a dexterous and fast walking. No model of the robot dynamics is engaged. The methodology in use is generic and can be applied to optimize control systems for diverse robots of comparable complexity.

Humanoid robots that have to operate in cluttered and unstructured environments, such as man-made and natural disaster scenarios, require sophisticated sensorimotor capabilities. A crucial prerequisite for the successful execution of whole-body locomotion and manipulation tasks in such environments is the perception of the environment and the extraction of associated environmental affordances, i.e., the action possibilities of the robot in the environment. We believe that such a coupling between perception and action could be a key to substantially increase the flexibility of humanoid robots.

In this paper, we approach the affordance-based generation of whole-body actions for stable locomotion and manipulation. We incorporate a rule-based system to assign affordance hypotheses to visually perceived environmental primitives in the scene. These hypotheses are then filtered using extended reachability maps that carry stability information, for identifying reachable affordance hypotheses. We then formulate the hypotheses in terms of a constrained inverse kinematics problem in order to find whole-body configurations that utilize a chosen set of hypotheses.

The proposed methods are implemented and tested in simulated environments based on RGB-D scans as well as on a real robotic platform.

Detecting affordances on objects is one of the main open problems in robotic manipulation. This paper presents a new method to represent and localize grasp affordances as bounded curved contact patches (paraboloids) of the size of the robotic hand. In particular, given a three-dimensional (3D) point cloud from a range sensor, a set of potential grasps is localized on a detected object by a fast contact patch fitting and validation process. For the object detection, three standard methods from the literature are used and compared. The potential grasps on the object are then refined to a single affordance using their shape (size and curvature) and pose (reachability and minimum torque effort) properties, with respect to the robot and the manipulation task. We apply the proposed method to a circular valve turning task, verifying the ability to accurately and rapidly localize grasp affordances, under significant uncertainty in the environment. We experimentally validate the method with the humanoid robot COMAN on 10 circular control valves fixed on a wall, from five different viewpoints and robot poses for each valve. We compare the reliability of the introduced local grasp affordances method to the baseline that relies only on object detection, illustrating the superiority of ours for the valve turning task.

Precise task-space tracking with manipulator-type systems requires an accurate kinematic model. In contrast to traditional manipulators, sometimes it is difficult to obtain an accurate kinematic model of humanoid robots due to complex structure and link flexibility. Also, prolonged use of the robot will lead to some parts wearing out or being replaced with a slightly different alignment, thus throwing off the initial calibration. Therefore, there is a need to develop a control algorithm that can compensate for the modeling errors and quickly retune itself, if needed, taking into account the controller bandwidth limitations and high dimensionality of the system. In this paper, we develop an iterative learning control algorithm that can work with existing inverse kinematics solvers to refine the joint-level control commands to enable precise tracking in the task space. We demonstrate the efficacy of the algorithm on a theme-park-type humanoid doing a drawing task, serving drink in a glass, and serving a drink placed on a tray without spilling. The iterative learning control algorithm is able to reduce the tracking error by at least two orders of magnitude in less than 20 trials.

Lifting objects, whose mass may produce high wrist torques that exceed the hardware strength limits, could lead to unstable grasps or serious robot damage. This work introduces a new Center-of-Mass (CoM)-based grasp pose adaptation method, for picking up objects using a combination of exteroceptive 3D perception and proprioceptive force/torque sensor feedback. The method works in two iterative stages to provide reliable and wrist torque efficient grasps. Initially, a geometric object CoM is estimated from the input range data. In the first stage, a set of hand-size handle grasps are localized on the object and the closest to its CoM is selected for grasping. In the second stage, the object is lifted using a single arm, while the force and torque readings from the sensor on the wrist are monitored. Based on these readings, a displacement to the new CoM estimation is calculated. The object is released and the process is repeated until the wrist torque effort is minimized. The advantage of our method is the blending of both exteroceptive (3D range) and proprioceptive (force/torque) sensing for finding the grasp location that minimizes the wrist effort, potentially improving the reliability of the grasping and the subsequent manipulation task. We experimentally validate the proposed method by executing a number of tests on a set of objects that include handles, using the humanoid robot WALK-MAN.

Most existing motion control methods for humanoids aim at avoiding falling. However, the humanoid is generally an unstable system that cannot completely avoid falling and it is difficult to cope with the sudden fall of a robot. This paper designs a planning method of fall protection for humanoids according to the human falling motion. This method changes the impact position between the robot and ground by adjusting the motion of the robot as it falls. To further reduce damage to the robot, an appropriate cushioning material is installed at the point of impact to buffer the robot. The effectiveness of the proposed method is verified for a BHR6P humanoid robot falling in simulations and experiments.

This paper explores the confluence of physical embodiment and social interaction in the context of Cognitive Developmental Humanoid Robotics (CDHR). By classifying varied interactions through developmental stages of the “self” and their interaction spheres, the discussion unearths profound insights into the composite nature of developmental processes. It presents a multi-dimensional exploration through different interaction scenarios, ranging from fetus-mother interactions to advanced large language models like ChatGPT, revealing the intrinsic connection between the physical and social realms of existence. In conclusion, this paper broadens the horizon of our understanding of the intricate interplays between physical embodiment and social interaction, setting the stage for more nuanced, ethically sound approaches and explorations in the realm of humanoid robotics and artificial intelligence.

Information technology can be classified into three categories; the physical signal processing, the logical symbol processing and the Kansei (emotional) information processing. Human communication belongs to the last one, as it contains Kansei information. To treat Kansei information, computer must have physical body because Kansei is strictly related to the fact that we have finite lifetime. Robot is one of the most suitable information terminals with multi-modal communication channel to realize a new type of man-machine interface with Kansei. This article describes some considerations on Kansei as the third target of information processing and introduces the Humanoid research in Waseda University with some recent results

In this paper, we propose a signal-based bipedal walking algorithm. In our approach, there is a central periodic signal used to synchronize other signals. In order to be able to model the system better, the motion is divided into four different components which are represented with different signals. While modeling the system, we keep it as much parametric as possible to make the system adptable to different environments. Since it is extremely difficult to find the optimum parameter set for different floors by hand, we applied an opti-mization procedure based on Evolutionary Strategies to improve the walking performance. The walking algorithm and the training procedure is validated by the experiments on Aldebaran Nao Humanoid Robot.