Stable bipedal walking is a key prerequisite for humanoid robots to reach their potential of being versatile helpers in our everyday environments. Bipedal walking is, however, a complex motion that requires the coordination of many degrees of freedom while it is also inherently unstable and sensitive to disturbances. The balance of a walking biped has to be constantly maintained. The most effective way of controlling balance are well timed and placed recovery steps -- capture steps -- that absorb the expense momentum gained from a push or a stumble. We present a bipedal gait generation framework that utilizes step timing and foot placement techniques in order to recover the balance of a biped even after strong disturbances. Our framework modifies the next footstep location instantly when responding to a disturbance and generates controllable omnidirectional walking using only very little sensing and computational power. We exploit the open-loop stability of a central pattern generated gait to fit a linear inverted pendulum model to the observed center of mass trajectory. Then, we use the fitted model to predict suitable footstep locations and timings in order to maintain balance while following a target walking velocity. Our experiments show qualitative and statistical evidence of one of the strongest push-recovery capabilities among humanoid robots to date.
This paper proposes a framework designed to generate a closed-loop walking engine for a humanoid robot. In particular, the core of this framework is an abstract dynamics model which is composed of two masses that represent the lower and the upper body of a humanoid robot. Moreover, according to the proposed dynamics model, the low-level controller is formulated as a Linear-Quadratic-Gaussian (LQG) controller that is able to robustly track the desired trajectories. Besides, this framework is fully parametric which allows using an optimization algorithm to find the optimum parameters. To examine…
Most humanoid robots have highly complicated structure and design of robots that are very similar to human is extremely difficult. In this paper, modelling of a general and comprehensive algorithm for control of humanoid robots is presented using Colored Petri Nets. For keeping dynamic balance of the robot, combination of Gyroscope and Accelerometer sensors are used in algorithm. Image processing is used to identify two fundamental issues: first, detection of target or an object which robot must follow; second, detecting surface of the ground so that walking robot could maintain its balance ju…
Discrete time model predictive control for humanoid walking with step adjustment
Robert Bosch Centre for Cyber Physical Systems, Indian Institute of Science（印度科学学院罗伯特·博世网络物理系统中心）；Computer Science and Automation, Indian Institute of Science（印度科学学院计算机科学与自动化系）
Heidelberg University（海德堡大学）；Fondazione Istituto Italiano di Tecnologia (IIT)（意大利理工学院）
The humanoid robot iCub is a research platform of the Fondazione Istituto Italiano di Tecnologia (IIT), spread among different institutes around the world. In the most recent version of iCub, the robot is equipped with stronger legs and bigger feet, allowing it to perform balancing and walking motions that were not possible with the first generations. Despite the new legs hardware, walking has been rarely performed on the iCub robot. In this work the objective is to implement walking motions on the robot, from which we want to analyze its walking capabilities. We developed software modules bas…
Istituto Italiano di Tecnologia（意大利理工学院）；Università degli Studi di Genova（热那亚大学）；Google DeepMind（谷歌DeepMind）
A common approach to the generation of walking patterns for humanoid robots consists in adopting a layered control architecture. This paper proposes an architecture composed of three nested control loops. The outer loop exploits a robot kinematic model to plan the footstep positions. In the mid layer, a predictive controller generates a Center of Mass trajectory according to the well-known table-cart model. Through a whole-body inverse kinematics algorithm, we can define joint references for position controlled walking. The outcomes of these two loops are then interpreted as inputs of a stack-…
Fondazione Istituto Italiano di Tecnologia（意大利技术研究院）
This paper contributes towards the development and comparison of Divergent-Component-of-Motion (DCM) based control architectures for humanoid robot locomotion. More precisely, we present and compare several DCM based implementations of a three layer control architecture. From top to bottom, these three layers are here called: trajectory optimization, simplified model control, and whole-body QP control. All layers use the DCM concept to generate references for the layer below. For the simplified model control layer, we present and compare both instantaneous and Receding Horizon Control controll…
This paper proposes a novel orientation-aware model predictive control (MPC) for dynamic humanoid walking that can plan footstep locations online. Instead of a point-mass model, this work uses the augmented single rigid body model (aSRBM) to enable the MPC to leverage orientation dynamics and stepping strategy within a unified optimization framework. With the footstep location as part of the decision variables in the aSRBM, the MPC can reason about stepping within the kinematic constraints. A task-space controller (TSC) tracks the body pose and swing leg references output from the MPC, while e…
This paper proposes an architecture for achieving telexistence and teleoperation of humanoid robots. The architecture combines several technological set-ups, methodologies, locomotion and manipulation algorithms in a novel manner, thus building upon and extending works available in literature. The approach allows a human operator to command and telexist with the robot. Therefore, in this work we treat aspects pertaining not only to the proposed architecture structure and implementation, but also the human operator experience in terms of ability to adapt to the robot and to the architecture. Al…
K. N. Toosi University of Technology（K. N. 图西理工大学）；University of Tehran（德黑兰大学）
In this paper, an online adaptation algorithm for bipedal walking on uneven surfaces with height uncertainty is proposed. In order to generate walking patterns on flat terrains, the trajectories in the task space are planned to satisfy the dynamic balance and slippage avoidance constraints, and also to guarantee smooth landing of the swing foot. To ensure smooth landing of the swing foot on surfaces with height uncertainty, the preplanned trajectories in the task space should be adapted. The proposed adaptation algorithm consists of two stages. In the first stage, once the swing foot reaches i…
Bipedal robots are essentially unstable because of their complex kinematics as well as high dimensional state space dynamics, hence control and generation of stable walking is a complex subject and still one of the active topics in the robotic community. Nowadays, there are many humanoids performing stable walking, but fewer show effective push recovery under pushes. In this paper, we firstly review more common used abstract dynamics models for a humanoid robot which are based on the inverted pendulum and show how these models can be used to provide walking for a humanoid robot and also how a …
Florida Institute for Human and Machine Cognition（佛罗里达人类与机器认知研究所）；Istituto Italiano di Tecnologia（意大利理工学院）
We present a method for humanoid robot walking on partial footholds such as small stepping stones and rocks with sharp surfaces. Our algorithm does not rely on prior knowledge of the foothold, but information about an expected foothold can be used to improve the stepping performance. After a step is taken, the robot explores the new contact surface by attempting to shift the center of pressure around the foot. The available foothold is inferred by the way in which the foot rotates about contact edges and/or by the achieved center of pressure locations on the foot during exploration. This estim…
Italian Institute of Technology（意大利技术研究院）；University of Manchester（曼彻斯特大学）；University of Genoa（热那亚大学）
The paper presents a planner to generate walking trajectories by using the centroidal dynamics and the full kinematics of a humanoid robot. The interaction between the robot and the walking surface is modeled explicitly via new conditions, the \emph{Dynamical Complementarity Constraints}. The approach does not require a predefined contact sequence and generates the footsteps automatically. We characterize the robot control objective via a set of tasks, and we address it by solving an optimal control problem. We show that it is possible to achieve walking motions automatically by specifying a m…
ECO: Energy-Constrained Optimization with Reinforcement Learning for Humanoid Walking
State Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence (BIGAI)（通用人工智能国家重点实验室，北京通用人工智能研究院（BIGAI））；Department of Automation, Tsinghua University（自动化系，清华大学）；Department of Automation, University of Science and Technology of China（自动化系，中国科学技术大学）；Department of Computer Science, Harbin Institute of Technology（计算机科学系，哈尔滨工业大学）；Institute for Artificial Intelligence and School of Artificial Intelligence, Peking University（人工智能研究院和人工智能学院，北京大学）
Robust Humanoid Walking on Compliant and Uneven Terrain with Deep Reinforcement Learning
CNRS-AIST JRL (Joint Robotics Laboratory) IRL, National Institute of Advanced Industrial Science and Technology (AIST), Japan（日本国家先进工业科学与技术研究院（AIST））；University of Tsukuba（筑波大学）；Department of Computer Science, Stanford University（斯坦福大学计算机科学系）
National School of Engineering of Sfax, University of Sfax（斯法克斯国家工程学院，斯法克斯大学）
In this chapter we will highlight our experimental studies on natural human walking analysis and introduce a biologically inspired design for simple bipedal locomotion system of humanoid robots. Inspiration comes directly from human walking analysis and human muscles mechanism and control. A hybrid algorithm for walking gaits generation is then proposed as an innovative alternative to classically used kinematics and dynamic equations solving, the gaits include knee, ankle and hip trajectories. The proposed algorithm is an intelligent evolutionary based on particle swarm optimization paradigm. …
Florida Institute for Human and Machine Cognition（佛罗里达人类与机器认知研究所）；Virginia Tech（弗吉尼亚理工大学）
While humans are highly capable of recovering from external disturbances and uncertainties that result in large tracking errors, humanoid robots have yet to reliably mimic this level of robustness. Essential to this is the ability to combine traditional "ankle strategy" balancing with step timing and location adjustment techniques. In doing so, the robot is able to step quickly to the necessary location to continue walking. In this work, we present both a new swing speed up algorithm to adjust the step timing, allowing the robot to set the foot down more quickly to recover from errors in the d…
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Stanford University（斯坦福大学）
Whole-body control (WBC) is a generic task-oriented control method for feedback control of loco-manipulation behaviors in humanoid robots. The combination of WBC and model-based walking controllers has been widely utilized in various humanoid robots. However, to date, the WBC method has not been employed for unsupported passive-ankle dynamic locomotion. As such, in this paper, we devise a new WBC, dubbed whole-body locomotion controller (WBLC), that can achieve experimental dynamic walking on unsupported passive-ankle biped robots. A key aspect of WBLC is the relaxation of contact constraints …
In this paper, we propose an efficient approach to generate dynamic and versatile humanoid walking with non-constant center of mass (COM) height. We exploit the benefits of using reduced order models (ROMs) and stepping control to generate dynamic and versatile walking motion. Specifically, we apply the stepping controller based on the Hybrid Linear Inverted Pendulum Model (H-LIP) to perturb a periodic walking motion of a 3D actuated Spring Loaded Inverted Pendulum (3D-aSLIP), which yields versatile walking behaviors of the 3D-aSLIP, including various 3D periodic walking, fixed location tracki…
One-shot Adaptation of Humanoid Whole-body Motion with Walking Priors
Embodied AI and Robotics (AIR) Lab, NYUAD（纽约大学阿布扎比分校具身人工智能与机器人实验室）；NYUAD Center for Artificial Intelligence and Robotics (CAIR)（纽约大学阿布扎比分校人工智能与机器人中心）；New York University Abu Dhabi（纽约大学阿布扎比分校）；Harvard Ophthalmology AI Lab, Harvard University（哈佛大学眼科人工智能实验室）
PolygMap: A Perceptive Locomotion Framework for Humanoid Robot Stair Climbing
The Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人社会研究院）；Electronic science and technology, Guangdong University of Technology（广东技术大学电子科学与技术学院）；Leju robotics（乐居机器人）
In this letter, we formulate a novel Markov Decision Process (MDP) for safe and data-efficient learning for humanoid locomotion aided by a dynamic balancing model. In our previous studies of biped locomotion, we relied on a low-dimensional robot model, commonly used in high-level Walking Pattern Generators (WPGs). However, a low-level feedback controller cannot precisely track desired footstep locations due to the discrepancies between the full order model and the simplified model. In this study, we propose mitigating this problem by complementing a WPG with reinforcement learning. More specif…
Florida Institute for Human and Machine Cognition（佛罗里达人机认知研究所）；University of West Florida（西佛罗里达大学）
In trying to build humanoid robots that perform useful tasks in a world built for humans, we address the problem of autonomous locomotion. Humanoid robot planning and control algorithms for walking over rough terrain are becoming increasingly capable. At the same time, commercially available depth cameras have been getting more accurate and GPU computing has become a primary tool in AI research. In this paper, we present a newly constructed behavior control system for achieving fast, autonomous, bipedal walking, without pauses or deliberation. We achieve this using a recently published rapid p…
iWalker: Imperative Visual Planning for Walking Humanoid Robot
The Spatial AI & Robotics (SAIR) Lab, Computer Science and Engineering, University at Buffalo（布法罗大学空间人工智能与机器人实验室）；The Wisconsin Expeditious Legged Locomotion (WELL) Lab, Mechanical Engineering, University of Wisconsin-Madison（威斯康星大学麦迪逊分校快速腿部运动实验室）
In this paper, 3D humanoid walking is decoupled into periodic and transitional motion, each of which is decoupled into planar walking in the sagittal and lateral plane. Reduced order models (ROMs), i.e. actuated Spring-loaded Inverted Pendulum (aSLIP) models and Hybrid-Linear Inverted Pendulum (H-LIP) models, are utilized for motion generation on the desired center of mass (COM) dynamics for each type of planar motion. The periodic motion is planned via point foot (underactuated) ROMs for dynamic motion with minimum ankle actuation, while the transitional motion is planned via foot-actuated RO…
The trend in the RoboCup Humanoid League rules over the past few years has been towards a more realistic and challenging game environment. Elementary skills such as visual perception and walking, which had become mature enough for exciting gameplay, are now once again core challenges. The field goals are both white, and the walking surface is artificial grass, which constitutes a much more irregular surface than the carpet used before. In this paper, team NimbRo TeenSize, the winner of the TeenSize class of the RoboCup 2016 Humanoid League, presents its robotic platforms, the adaptations that …
Istituto Italiano di Tecnologia（意大利理工学院）；University of Genoa（热那亚大学）
This manuscript presents a model of compliant contacts for time-critical humanoid robot motion control. The proposed model considers the environment as a continuum of spring-damper systems, which allows us to compute the equivalent contact force and torque that the environment exerts on the contact surface. We show that the proposed model extends the linear and rotational springs and dampers - classically used to characterize soft terrains - to the case of large contact surface orientations. The contact model is then used for the real-time whole-body control of humanoid robots walking on visco…
Foundation for Research and Technology - Hellas (FORTH)（希腊研究与技术基金会）；University College London（伦敦大学学院）
In this article, we propose a deep learning framework that provides a unified approach to the problem of leg contact detection in humanoid robot walking gaits. Our formulation accomplishes to accurately and robustly estimate the contact state probability for each leg (i.e., stable or slip/no contact). The proposed framework employs solely proprioceptive sensing and although it relies on simulated ground-truth contact data for the classification process, we demonstrate that it generalizes across varying friction surfaces and different legged robotic platforms and, at the same time, is readily t…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
For humanoids to be deployed in demanding situations, such as search and rescue, highly intelligent decision making and proficient sensorimotor skill is expected. A promising solution is to leverage human prowess by interconnecting robot and human via teleoperation. Towards creating seamless operation, this paper presents a dynamic telelocomotion framework that synchronizes the gait of a human pilot with the walking of a bipedal robot. First, we introduce a method to generate a virtual human walking model from the stepping behavior of a human pilot which serves as a reference for the robot to …
National Institute of Advanced Industrial Science and Technology (AIST)（产业技术综合研究所）；University of Tsukuba（筑波大学）
Deep reinforcement learning (RL) based controllers for legged robots have demonstrated impressive robustness for walking in different environments for several robot platforms. To enable the application of RL policies for humanoid robots in real-world settings, it is crucial to build a system that can achieve robust walking in any direction, on 2D and 3D terrains, and be controllable by a user-command. In this paper, we tackle this problem by learning a policy to follow a given step sequence. The policy is trained with the help of a set of procedurally generated step sequences (also called foot…
Istituto Italiano di Tecnologia（意大利理工学院）；University of Genoa（热那亚大学）；University of Manchester（曼彻斯特大学）
This paper presents a Non-Linear Model Predictive Controller for humanoid robot locomotion with online step adjustment capabilities. The proposed controller considers the Centroidal Dynamics of the system to compute the desired contact forces and torques and contact locations. Differently from bipedal walking architectures based on simplified models, the presented approach considers the reduced centroidal model, thus allowing the robot to perform highly dynamic movements while keeping the control problem still treatable online. We show that the proposed controller can automatically adjust the …
University of Southern California（南加州大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Torque control algorithms which consider robot dynamics and contact constraints are important for creating dynamic behaviors for humanoids. As computational power increases, algorithms tend to also increase in complexity. However, it is not clear how much complexity is really required to create controllers which exhibit good performance. In this paper, we study the capabilities of a simple approach based on contact consistent LQR controllers designed around key poses to control various tasks on a humanoid robot. We present extensive experimental results on a hydraulic, torque controlled humano…
Korea Advanced Institute of Science and Technology（韩国科学技术院）；Korea Atomic Energy Research Institute（韩国原子能研究院）
Although bipedal locomotion provides the ability to traverse unstructured environments, it requires careful planning and control to safely walk across without falling. This poses an integrated challenge for the robot to perceive, plan, and control its movements, especially with dynamic motions where the robot may have to adapt its swing-leg trajectory onthe-fly in order to safely place its foot on the uneven terrain. In this paper we present an efficient geometric footstep planner and the corresponding walking controller that enables a humanoid robot to dynamically walk across uneven terrain a…
Montpellier Laboratory of Informatics, Robotics and Microelectronics (LIRMM), CNRS–University of Montpellier（蒙彼利埃信息学、机器人学与微电子实验室（LIRMM），法国国家科学研究中心-蒙彼利埃大学）；CNRS–AIST Joint Robotics Laboratory (JRL)（法国国家科学研究中心-日本产业技术综合研究所联合机器人实验室（JRL））
We consider dynamic stair climbing with the HRP-4 humanoid robot as part of an Airbus manufacturing use-case demonstrator. We share experimental knowledge gathered so as to achieve this task, which HRP-4 had never been challenged to before. In particular, we extend walkingstabilization based on linear inverted pendulum tracking by quadratic programming-based wrench distribution and a whole-body admittance controller that applies both end-effector and CoM strategies. While existing stabilizers tend to use either one or the other, our experience suggests that the combination of these two approac…
Learning Bipedal Walking for Humanoids with Current Feedback
CNRS-AIST JRL (Joint Robotics Laboratory) IRL（法国国家科学研究中心-产业技术综合研究所联合机器人实验室国际联合实验室）；National Institute of Advanced Industrial Science and Technology (AIST)（产业技术综合研究所）；University of Tsukuba（筑波大学）；Stanford University（斯坦福大学）
The Cloud-based Advanced Robotics Laboratory (CARL) integrates a whole body controller and web-based teleoperation to enable any device with a web browser to access and control a humanoid robot. By integrating humanoid robots with the cloud, they are accessible from any Internet-connected device. Increased accessibility is important because few people have access to state-of-the-art humanoid robots limiting their rate of development. CARL's implementation is based on modern software libraries, frameworks, and middleware including Node.js, Socket.IO, ZMQ, ROS, Robot Web Tools, and ControlIt! Fe…
A common architecture for torque controlled humanoid robots consists in two nested loops. The outer loop generates desired joint/motor torques, and the inner loop stabilises these desired values. In doing so, the inner loop usually compensates for joint friction phenomena, thus removing their inherent stabilising property that may be also beneficial for high level control objectives. This paper shows how to exploit friction for joint and task space control of humanoid robots. Experiments are carried out using the humanoid robot iCub.
In this paper, a combination of ankle and hip strategy is used for push recovery of a position-controlled humanoid robot. Ankle strategy and hip strategy are equivalent to Center of Pressure (CoP) and Centroidal Moment Pivot (CMP) regulation respectively. For controlling the CMP and CoP we need a torque-controlled robot, however most of the conventional humanoid robots are position controlled. In this regard, we present an efficient way for implementation of the hip and ankle strategies on a position controlled humanoid robot. We employ a feedback controller to compensate the capture point err…
