Robot gait optimization is the task of generating an optimal control trajectory under various internal and external constraints. Given the high dimensions of control space, this problem is particularly challenging for multi-legged robots walking in complex and unknown environments. Existing literatures often regard the gait generation as an optimization problem and solve the gait optimization from scratch for robots walking in a specific environment. However, such approaches do not consider the use of pre-acquired knowledge which can be useful in improving the quality and speed of motion generation in complex environments. To address the issue, this paper proposes a transfer learning-based evolutionary framework for multi-objective gait optimization, named Tr-GO. The idea is to initialize a high-quality population by using the technique of transfer learning, so any kind of population-based optimization algorithms can be seamlessly integrated into this framework. The advantage is that the generated gait can not only dynamically adapt to different environments and tasks, but also simultaneously satisfy multiple design specifications (e.g., speed, stability). The experimental results show the effectiveness of the proposed framework for the gait optimization problem based on three multi-objective evolutionary algorithms: NSGA-II, RM-MEDA and MOPSO. When transferring the pre-acquired knowledge from the plain terrain to various inclined and rugged ones, the proposed Tr-GO framework accelerates the evolution process by a minimum of 3-4 times compared with non-transferred scenarios.
University of Oslo（奥斯陆大学）；Norwegian Defence Research Institute（挪威国防研究所）
Legged robots promise higher versatility and the ability to traverse much more difficult terrains than their wheeled counterparts. Even though the use of legged robots have increased drastically in the last few years, they are still not close to the performance seen from legged animals in nature. Robotic legs are typically fairly simple mechanically, and few feature an ankle joint, even though most land mammals have one. The ankle could be a key to better performance and stability for legged robots, and in this paper we investigate how the use of an ankle in a bipedal robot could improve its p…
Legged locomotion is commonly studied and expressed as a discrete set of gait patterns, like walk, trot, gallop, which are usually treated as given and pre-programmed in legged robots for efficient locomotion at different speeds. However, fixing a set of pre-programmed gaits limits the generality of locomotion. Recent animal motor studies show that these conventional gaits are only prevalent in ideal flat terrain conditions while real-world locomotion is unstructured and more like bouts of intermittent steps. What principles could lead to both structured and unstructured patterns across mammal…
Istituto Italiano di Tecnologia（意大利技术研究院）；Università degli Studi di Genova（热那亚大学）；Swiss Federal Institute of Technology at Lausanne（洛桑联邦理工学院）；University of Texas（德克萨斯大学）；University of Naples Federico II（那不勒斯费德里科二世大学）
(Simplified Abstract) To accomplish breakthroughs in dynamic whole-body locomotion, legged robots have to be terrain aware. Terrain-Aware Locomotion (TAL) implies that the robot can perceive the terrain with its sensors, and can take decisions based on this information. This thesis presents TAL strategies both from a proprioceptive and an exteroceptive perspective. The strategies are implemented at the level of locomotion planning, control, and state estimation, and using optimization and learning techniques. The first part is on TAL strategies at the Whole-Body Control (WBC) level. We introdu…
Optimizing gait stability for legged robots is a difficult problem. Even on level surfaces, effectively traversing across different textures (e.g., carpet) rests on dynamically tuning parameters in multidimensional space. Inspired by biology, evolutionary algorithms (EA) remain an attractive solution for feasibly implementing robotic locomotion with both energetic economy and rapid parameter convergence. Here, we leveraged this class of algorithms to evolve a stable hexapod gait controller capable of traversing uneven terrain and obstacles. Gait parameters were evolved in a rigid body dynamics…
This work reports on developing a deep inverse reinforcement learning method for legged robots terrain traversability modeling that incorporates both exteroceptive and proprioceptive sensory data. Existing works use robot-agnostic exteroceptive environmental features or handcrafted kinematic features; instead, we propose to also learn robot-specific inertial features from proprioceptive sensory data for reward approximation in a single deep neural network. Incorporating the inertial features can improve the model fidelity and provide a reward that depends on the robot's state during deployment…
This work is on vision-based planning strategies for legged robots that separate locomotion planning into foothold selection and pose adaptation. Current pose adaptation strategies optimize the robot's body pose relative to given footholds. If these footholds are not reached, the robot may end up in a state with no reachable safe footholds. Therefore, we present a Vision-Based Terrain-Aware Locomotion (ViTAL) strategy that consists of novel pose adaptation and foothold selection algorithms. ViTAL introduces a different paradigm in pose adaptation that does not optimize the body pose relative t…
Soft pneumatic legged robots show promise in their ability to traverse a range of different types of terrain, including natural unstructured terrain met in applications like precision agriculture. They can adapt their body morphology to the intricacies of the terrain at hand, thus enabling robust and resilient locomotion. In this paper we capitalize upon recent developments on soft pneumatic legged robots to introduce a closed-loop trajectory tracking control scheme for operation over flat ground. Closed-loop pneumatic actuation feedback is achieved via a compact and portable pneumatic regulat…
Despite many accomplishments by legged robot designers, state-of-the-art bipedal robots are prone to falling over, cannot negotiate extremely rough terrains and cannot directly regulate unilateral contact forces. Our objective is to integrate merits of legged and aerial robots in a single platform. We will show that the thrusters in a bipedal legged robot called Harpy can be leveraged to stabilize the robot's frontal dynamics and permit jumping over large obstacles which is an unusual capability not reported before. In addition, we will capitalize on the thrusters action in Harpy and will show…
PIE: Parkour with Implicit-Explicit Learning Framework for Legged Robots
Institute of Cyber-Systems and Control, Zhejiang University（浙江大学控制科学与工程学院 cyber-system 研究所）；State Key Laboratory of Industrial Control Technology（工业控制技术国家重点实验室）
Real-time Digital Double Framework to Predict Collapsible Terrains for Legged Robots
National University of Singapore (NUS)（新加坡国立大学）；Institute for Infocomm Research (I 2 R)（信息通信研究所）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）
In this paper, we propose a novel approach on controlling wheel-legged quadrupedal robots using pose optimization and force control via quadratic programming (QP). Our method allows the robot to leverage the whole-body motion and the wheel actuation to roll over high obstacles while keeping the wheel torques to navigate the terrain while keeping the wheel traction and balancing the robot body. In detail, we first present a linear rigid body dynamics with wheels that can be used for real-time balancing control of wheel-legged robots. We then introduce an effective pose optimization method for w…
FZI Research Center for Information Technology（弗劳恩霍夫研究所信息技术研究中心）；Machine Intelligence and Robotics Lab at the Karlsruhe Institute for Technology (KIT)（卡尔斯鲁厄理工学院机械智能与机器人实验室）
Effect of Gait Design on Proprioceptive Sensing of Terrain Properties in a Quadrupedal Robot
Department of Mechanical Engineering, Massachusetts Institute of Technology（机械工程系，麻省理工学院）；Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California（Ming Hsieh 电子与计算机工程系，南加州大学）；Department of Earth and Environmental Science, University of Pennsylvania（地球与环境科学系，宾夕法尼亚大学）
本文对比四足机器人的Crawl N' Sense与Trot-Walk步态，证明慢速爬行步态能更稳定、准确地感知可变形地形强度及表面结壳破裂。
Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；National University of Singapore（新加坡国立大学）；Carnegie Mellon University（卡内基梅隆大学）；Morehouse College（莫尔豪斯学院）；Pennsylvania State University（宾夕法尼亚州立大学）；Rancho Research Institute（兰乔研究所）；Royal Veterinary College（皇家兽医学院）；University of Oviedo（奥维耶多大学）
Serially connected robots are promising candidates for performing tasks in confined spaces such as search-and-rescue in large-scale disasters. Such robots are typically limbless, and we hypothesize that the addition of limbs could improve mobility. However, a challenge in designing and controlling such devices lies in the coordination of high-dimensional redundant modules in a way that improves mobility. Here we develop a general framework to control serially connected multi-legged robots. Specifically, we combine two approaches to build a general shape control scheme which can provide baselin…
The recent availability of commercial-off-the-shelf (COTS) legged robot platforms have opened up new opportunities in deploying legged systems into different scenarios. While the main advantage of legged robots is their ability to traverse unstructured terrain, there are still large gaps between what robot platforms can achieve and their animal counterparts. Therefore, when deploying as part of a heterogeneous robot team of different platforms, it is beneficial to understand the different scenarios where a legged platform would perform better than a wheeled, tracked or aerial platform. Two COT…
Our paper proposes a model predictive controller as a single-task formulation that simultaneously optimizes wheel and torso motions. This online joint velocity and ground reaction force optimization integrates a kinodynamic model of a wheeled quadrupedal robot. It defines the single rigid body dynamics along with the robot's kinematics while treating the wheels as moving ground contacts. With this approach, we can accurately capture the robot's rolling constraint and dynamics, enabling automatic discovery of hybrid maneuvers without needless motion heuristics. The formulation's generality thro…
University of Lisbon（里斯本大学）；Instituto Superior Técnico（高等技术学院）
We present a solvable biped walking model based on an inverted pendulum with two massless articulated legs capable of walking on uneven floors and inclined planes. The stride of the two-legged robot results from the pendular motion of a standing leg and the articulated motion of a trailing leg. Gaiting is possible due to the alternating role of the legs, the standing and the trailing leg, and the conservation of energy of the pendular motion. The motion on uneven surfaces and inclined planes is possible by imposing the same maximal opening angle between the two legs in the transition between s…
Legged robots are popular candidates for missions in challenging terrains due to the wide variety of locomotion strategies they can employ. Terrain classification is a key enabling technology for autonomous legged robots, as it allows the robot to harness their innate flexibility to adapt their behaviour to the demands of their operating environment. In this paper, we show how highly capable machine learning techniques, namely gated recurrent neural networks, allow our target legged robot to correctly classify the terrain it traverses in both supervised and semi-supervised fashions. Tests on a…
ATRos: Learning Energy-Efficient Agile Locomotion for Wheeled-legged Robots
Shanghai Huawei Technologies Co., Ltd.（上海华为技术有限公司）；College of Future Information Technology, Fudan University（复旦大学未来信息科技学院）；Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；School of Aeronautic and Astronautics, Zhejiang University（浙江大学航空宇航学院）；School of Electronics and Communication Engineering, Sun Yat-sen University（中山大学电子与通信工程学院）
Safety concerns during the operation of legged robots must be addressed to enable their widespread use. Machine learning-based control methods that use model-based constraints provide promising means to improve robot safety. This study presents a modular safety filter to improve the safety of a legged robot, i.e., reduce the chance of a fall. The prerequisite is the availability of a robot that is capable of locomotion, i.e., a nominal controller exists. During locomotion, terrain properties around the robot are estimated through machine learning which uses a minimal set of proprioceptive sign…
Successful real-world deployment of legged robots would require them to adapt in real-time to unseen scenarios like changing terrains, changing payloads, wear and tear. This paper presents Rapid Motor Adaptation (RMA) algorithm to solve this problem of real-time online adaptation in quadruped robots. RMA consists of two components: a base policy and an adaptation module. The combination of these components enables the robot to adapt to novel situations in fractions of a second. RMA is trained completely in simulation without using any domain knowledge like reference trajectories or predefined …
SlipSense: Multimodal Sensing for Online Slip Detection in Legged Robots
School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Institute for Infocomm Research, Agency for Science Technology and Research（资讯通信研究院，科技研究局）
This paper introduces a novel proprioceptive state estimator for legged robots based on a learned displacement measurement from IMU data. Recent research in pedestrian tracking has shown that motion can be inferred from inertial data using convolutional neural networks. A learned inertial displacement measurement can improve state estimation in challenging scenarios where leg odometry is unreliable, such as slipping and compressible terrains. Our work learns to estimate a displacement measurement from IMU data which is then fused with traditional leg odometry. Our approach greatly reduces the …
Learning to enhance multi-legged robot on rugged landscapes
Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（佐治亚理工学院机器人与智能机器研究所）；School of Physics, Georgia Institute of Technology（佐治亚理工学院物理学院）；School of Computing, Georgia Institute of Technology（佐治亚理工学院计算学院）
Real-time Neural Dense Elevation Mapping for Urban Terrain with Uncertainty Estimations
Hong Kong University of Science and Technology（香港科技大学）；Clear Water Bay Institute of Autonomous Driving（清水湾自动驾驶研究院）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳研究院）
Terrain-Aware Quadrupedal Locomotion via Reinforcement Learning
Tencent Robotics X（腾讯Robotics X）；The Chinese University of Hong Kong（香港中文大学）；Southern University of Science and Technology（南方科技大学）；University of Alberta（阿尔伯塔大学）
Walk Like Dogs: Learning Steerable Imitation Controllers for Legged Robots from Unlabeled Motion Data
Computational Robotics Lab in the Department of Computer Science, ETH Zurich, Switzerland（计算机科学系计算机器人实验室，瑞士苏黎世联邦理工学院）；Robot Intelligence Lab in the Department of Artificial Intelligence, Korea University, South Korea（人工智能系机器人智能实验室，韩国大学）
