Collaborative robots can relief human operators from excessive efforts during payload lifting activities. Modelling the human partner allows the design of safe and efficient collaborative strategies. In this paper, we present a control approach for human-robot collaboration based on human monitoring through whole-body wearable sensors, and interaction modelling through coupled rigid-body dynamics. Moreover, a trajectory advancement strategy is proposed, allowing for online adaptation of the robot trajectory depending on the human motion. The resulting framework allows us to perform payload lifting tasks, taking into account the ergonomic requirements of the agents. Validation has been performed in an experimental scenario using the iCub3 humanoid robot and a human subject sensorized with the iFeel wearable system.

A multi-fingered hand that can grasp and manipulate a variety of objects is an option for assisting people in their daily lives. However, the range of torque output that can be handled by the multi-fingered hand is very limited compared to the capability of the human hand. In this paper, we introduce a new multi-fingered hand consisting of a dynamic pulley and a linkage mechanism, aiming to achieve a human-like output torque with a human-like size. The proposed multi-fingered hand can achieve a fingertip force of 50N, which is equivalent to that of a human, and at the same time can perform delicate operations such as picking up a coin on a desk. In addition, we realized the stay-on-tab opening task of a can by utilizing fingertip strength.

In this paper, we present a data acquisition method to realize a distributed tactile sensor system that can provide wide-range, and high-sensitivity with a small data size for communication. Since the data size is proportional to the number of acquired data and the resolution of the data, we propose systems to increase the resolution of the sensor output values without increasing the amount of data and to reduce the sampling frequency without reducing the time resolution. The system to increase the data resolution is based on a mechanism to dynamically change the gain of an amplifier circuit for a tactile sensor depending on the input value in the local controller. The system to reduce the number of data acquisitions consists of an analog circuit to judge the amount of change in the tactile sensor output based on a threshold at the hardware level. We demonstrate that the system reduces the data size for communication on a sensor system in our robotic hand. The experimental results indicate that the tactile sensor systems could sense high-resolution data with a small data size for communication.

Humans show specialized strategies for efficient collaboration. Transferring similar strategies to humanoid robots can improve their capability to interact with other agents, leading the way to complex collaborative scenarios with multiple agents acting on a shared environment. In this paper we present a control framework for robot-robot collaborative lifting. The proposed shared controller takes into account the joint action of both the robots thanks to a centralized controller that communicates with them, and solves the whole-system optimization. Efficient collaboration is ensured by taking into account the ergonomic requirements of the robots through the optimization of posture and contact forces. The framework is validated in an experimental scenario with two iCub humanoid robots performing different payload lifting sequences.

It is necessary to use not only foot but also hand, knee and other body parts to support body weight for locomotion in uneven terrain. Such multi-contact motion planning is an important research topic including lots of previous works; however, a problem of computational speed of planning is still remaining. In this paper, we propose a learning-based algorithm to speed up the planning. The algorithm reduces replanning of contact states by learning an evaluation function of key pose to reach goal. We investigated the learning performance by comparing three neural network configurations and two activation function. This research aims at achieving robust robotics system in unknown environments.

Articulated robots such as the humanoid robot often have multiple joints implemented in its limbs. It is common for sensor and controller cables to be routed inside of the limb, impacting mobility. A network protocol with high efficiency, low latency, fault tolerance is needed to facilitate communication of sensor and controller data over a more compact communications channel. In this paper, we propose a ring network protocol with fault tolerance in low latency, high efficiency communication over a compact channel. The performance of the proposed network protocol was verified in simulation and experiment, showing that low latency, high efficiency, and fault tolerance can be achieved over a single communications channel.

Real-time planning of footsteps has been a big challenge for bipedal research. A large number of methods have been proposed over the last several years. In addition, divergent component of motion (DCM) of linear inverted pendulum (LIP) has been applied to solve this problem thus attracting a great deal of attention. In this paper, we derive an analytical solution of DCM for an arbitrary input function and propose a novel quadratic programming (QP) problem for the simultaneous optimization of zero moment point (ZMP) and foot placements based on the analytical solution. To validate the method, we conducted a push recovery experiment on real hardware. The result of the experiment shows that our new algorithm realizes a hierarchical strategy for disturbance compensation.

Applying robots in narrow and cluttered disaster environments such as oil refineries requires a slim body and a wide range of motion. It is also necessary to have abilities to absorb unexpected contact with the environment and to walk on scattered debris. In this paper we propose new compact and high performance torque-controlled actuators for legged robots to satisfy the above mentioned requirements. For axial compactness, torque sensors are designed as ring-shaped thin cylinders surrounding motors or gears with strain gauges for sensing. To achieve broad bandwidth of torque control, we introduced an analog differentiator circuit into an analog digital converter (ADC) board in order to suppress noise in the differential control of joint torque. We also propose methods to reduce torque ripple caused by the deformation of the harmonic drive gear and electromagnetic interference (EMI) from a motor and a motor driver. Finally, experiments of a collision with objects and movement on scattered debris were executed with a fully torque-controlled legged robot built with the proposed actuators.

In this paper, a new experimental legged robot for inspection and disaster response in social infrastructures designed for humans, such as factories and power plants is presented. The robot has distinctive features to climb up and down ladders with a narrow cage, to pass through narrow environments, to move upon scattered debris at disaster sites and to deal with sudden and unexpected contact. The robot also satisfies environmental requirements related to dust, water, and temperature. To implement environmental resistance to a slim body for movement in a narrow environment, cooling of heat sources such as CPUs is a serious problem. Therefore a new cooling structure for humanoid robots is proposed. The proposed robot experiments on the above features are also shown in this paper.

Applying humanoid robots in disaster sites requires strong locomotion abilities due to the cluttered and unstructured environment. Besides bipedal walking, it is beneficial to provide functionality to locomote on four limbs, and to control the transitions between bipedal and quadrupedal locomotion. In this paper, we propose a planning algorithm for bipedal and quadrupedal locomotion with intermediate transitions. The algorithm is based on the divergent component of motion for a linear time-variant inverted pendulum model with variable height and a flywheel. The model was validated by model comparisons in a simulation experiment. In addition, the algorithm was validated on a real robot, which realized a sequence of bipedal and quadrupedal locomotion without intermediate stops.

This paper presents a novel control method to stabilize the whole-body motion of humanoid robots when climbing vertical ladders and transitioning between ladders and catwalks. In such environments, the body of the robot tends to incline and rotate because of the slippery surfaces. The inclination and rotation may cause the robot to fail to grasp and thus collide with the rungs. The proposed method modifies the subsequent contact position in real time based on the error of the current robot posture estimated with inertial measurement units (IMUs) and actual joint angles. This paper also presents a method of generating motion by minimizing the contact wrench. This method satisfies hardware limitations, such as collision avoidance, joint torque limits, and joint limits. Applying these methods to a humanoid robot, we realize the robust climbing and descending of multiple rungs of a vertical ladder and bidirectional transitioning from ladders to catwalks.

This paper introduces a motion generation framework that integrates a hierarchical movement primitive (MP) layer with optimal control in form of receding horizon optimization. In order to benefit from fast reactions on the MP-layer, the optimal control layer can be overridden in risky situations to generate quick, though non-optimal solutions. By this, the system fulfills four desirable properties. It continuously adapts the robot's motion without noticeable delay (1) by optimizing for collision and joint limit avoidance based on a future time horizon instead of the current state only (2). It accounts for the full robot motion that may result from multiple active MPs at the same time (3) and despite a possibly slow optimization still provides the robustness and quick reaction capabilities of MPs (4). The framework has been validated in an experiment in which a humanoid robot performed a task, optimized wrt. collisions and joint limit avoidance, but still could react within 50 ms after detection of a potential risk.

This paper addresses the question of how to create adaptive and smooth sequences of actions and how to decide among skill options in a continuous manner without the necessity of recurrent planning. Motion generation is based on serial and parallel blending of movement primitives (MP). MPs are modeled as dynamical systems on task coordinates with attractor behavior and augmented with additional signals to ease their coordination. Sequences and transitions between skills are realized in a unified way as bifurcating dynamical systems based on continuous-time recurrent neural networks. The neural output is used as activation signal for MPs. Besides continuous feedback from the controlled MPs, the neural dynamics is influenced by a cost term from a future prediction to allow the inhibition of an action flow that is expected to fail. First results are shown in a physical simulation environment on a high-DoF robotic hand-arm system. The system is capable of creating smooth transients of MPs. Robustness to disturbances can be observed as local adaptations of individual low-level MPs, flexible sequencing of MPs, and global error recovery by changing the whole strategy of how to perform a movement skill.

Generating stable dynamic motions for a biped robot in real time is difficult due to the unstable nature of biped systems and their high degrees of freedom. We propose an approximate dynamics model for biped robots with three masses and no kinematic constraints. We also propose a relaxed boundary condition called ¿the divergent component of motion¿. These techniques allow us to generate walking gait patterns with large margin from the edges of support polygon in real time.

Trajectories generated from approximate dynamics models can lead biped robots to fall down due to the difference of dynamics between the approximate dynamics model and the real robot. In this paper, we propose real time methods to compensate for the dynamics error using dynamics error compensation models. Our methods satisfy the horizontal ground reaction force and moment limits so that no slip is caused with the ground. We also propose a method to compensate for the knee dynamics error which is not modeled in our approximate dynamics models. Combining these techniques, running motion is achieved on a real biped.

A controller for biped running has to consider varying vertical ground reaction force while satisfying the horizontal ground reaction force and moment limits. We propose a design technique for feedback gains to stabilize the upper body position under varying vertical ground reaction force. We also propose an extended model ZMP control method which uses horizontal and rotational acceleration of the upper body and step duration change to generate moments to handle disturbances too large to be handled by ground reaction force control. Combining these techniques, robust biped running is achieved.

Bipedal running can easily result in a fall due to poor availability of the ground reaction force at the boundary of the flight and support phases. We propose methods to decompose and synthesize a running gait pattern into vertical, horizontal and rotational components so that time-dependent ground friction limits are satisfied. We also extend previously proposed boundary condition, the divergent component of motion, for switching walking gait patterns into running which involves vertical acceleration of the center of gravity. Using these techniques, running at 10 km/h is achieved on a real robot whose dimension are same as ASIMO.