Humanoid robot

About: Humanoid robot is a research topic. Over the lifetime, 14387 publications have been published within this topic receiving 243674 citations. The topic is also known as: 🤖.

Whole-body imitation of human motions with a nao humanoid

Abstract: We present a system that enables a humanoid robot to imitate complex whole-body motions of humans in real time. For recording the human motions, any sensor system capable of inferring the joint angle trajectories can be used. In our work, we capture the human data with an Xsens MVN motion capture system consisting of inertial sensors attached to the body. Our framework converts the human joint angles to the robot's joint angles in real time. Here, we use a mapping between the human's and the robot's joints to ensure feasibility of the motion. The focus of our system lies in ensuring static stability when the motions are executed which is a challenging task, depending on the complexity of the movements. To avoid falls of the robot that might occur when using direct imitation of the joint angle trajectories due to the different weight distribution, we developed an approach that actively balances the center of mass over the support polygon of the robot's feet. At every point in time, our approach ensures that the robot is in a statically stable configuration, i.e., that the ground projection of the center of mass lies within the convex hull of the foot contact points. To achieve this, we apply inverse kinematics given valid foot positions that satisfy the stability criterion and generate the corresponding leg joint angles. In more detail, our system first finds valid positions for the robot's feet by determining a target plane and its orientation, so that the feet can be placed planar and the robot's center of mass is over the support polygon. The new positions of the feet are chosen as the projection on the target plane. Afterwards, the corresponding leg joint angles are calculated via inverse kinematics. To determine whether the configuration is in the double support modus, and if not, which foot is the stance foot, we evaluate the position of the center of mass relative to the feet. As can be seen in the experiments with a Nao humanoid, our approach leads to a highly stable imitation of challenging human movements (see also Fig. 1). In contrast to recent approaches that capture human data using a Kinect-like sensor and only imitate arm movements while keeping the body static, our system can deal with complex, whole-body motions. Note that our approach does not require a prior learning phase but computes stable configurations online and almost in real time as can be seen in the accompanying video. We are currently working on imitating motions to learn complex navigation actions such as climbing up staircases or walking down ramps. Our system can also be used for tele-operated tasks that include whole-body movements where stability needs to be guaranteed in order to successfully fulfill the mission.

Humanoid navigation with dynamic footstep plans

Abstract: Humanoid robots possess the capability of stepping over or onto objects, which distinguishes them from wheeled robots. When planning paths for humanoids, one therefore should consider an intelligent placement of footsteps instead of choosing detours around obstacles. In this paper, we present an approach to optimal footstep planning for humanoid robots. Since changes in the environment may appear and a humanoid may deviate from its originally planned path due to imprecise motion execution or slippage on the ground, the robot might be forced to dynamically revise its plans. Thus, efficient methods for planning and replanning are needed to quickly adapt the footstep paths to new situations. We formulate the problem of footstep planning so that it can be solved with the incremental heuristic search method D* Lite and present our extensions, including continuous footstep locations and efficient collision checking for footsteps. In experiments in simulation and with a real Nao humanoid, we demonstrate the effectiveness of the footstep plans computed and revised by our method. Additionally, we evaluate different footstep sets and heuristics to identify the ones leading to the best performance in terms of path quality and planning time. Our D* Lite algorithm for footstep planning is available as open source implementation.

Whole body motion controller with long-term balance constraints

Abstract: The standard approach to real-time control of humanoid robots relies on approximate models to produce a motion plan, which is then used to control the whole body. Separation of the planning stage from the controller makes it difficult to account for the whole body motion objectives and constraints in the plan. For this reason, we propose to omit the planning stage and introduce long-term balance constraints in the whole body controller to compensate for this omission. The new controller allows for generation of whole body walking motions, which are automatically decided based on both the whole body motion objectives and balance preservation constraints. The validity of the proposed approach is demonstrated in simulation in a case where the walking motion is driven by a desired wrist position. This approach is general enough to allow handling seamlessly various whole body motion objectives, such as desired head motions, obstacle avoidance for all parts of the robot, etc.

Decentralized Robust Fuzzy Adaptive Control of Humanoid Robot Manipulation With Unknown Actuator Backlash

Abstract: Due to the fact that backlash nonlinearity is widespread in actuators, it is impossible to ignore its existence and achieve excellent and desirable mechanical system performance. In this paper, the problem of the humanoid robot grasping a common object with unknown actuator backlash is investigated. To tackle the nonsmooth backlash nonlinearity, a smooth adaptive backlash inverse is incorporated to compensate the line-segment effect. Moreover, a decentralized robust fuzzy adaptive control is constructed and developed to guarantee the object's motion and internal forces converge to the predefined values. The stabilities of the signals in the closed-loop system are proven by utilizing the Lyapunov method. In the end, experiments and simulations involving humanoid robot manipulation are conducted to validate the effectiveness of the proposed algorithms.