651 research outputs found
Goal Set Inverse Optimal Control and Iterative Re-planning for Predicting Human Reaching Motions in Shared Workspaces
To enable safe and efficient human-robot collaboration in shared workspaces
it is important for the robot to predict how a human will move when performing
a task. While predicting human motion for tasks not known a priori is very
challenging, we argue that single-arm reaching motions for known tasks in
collaborative settings (which are especially relevant for manufacturing) are
indeed predictable. Two hypotheses underlie our approach for predicting such
motions: First, that the trajectory the human performs is optimal with respect
to an unknown cost function, and second, that human adaptation to their
partner's motion can be captured well through iterative re-planning with the
above cost function. The key to our approach is thus to learn a cost function
which "explains" the motion of the human. To do this, we gather example
trajectories from pairs of participants performing a collaborative assembly
task using motion capture. We then use Inverse Optimal Control to learn a cost
function from these trajectories. Finally, we predict reaching motions from the
human's current configuration to a task-space goal region by iteratively
re-planning a trajectory using the learned cost function. Our planning
algorithm is based on the trajectory optimizer STOMP, it plans for a 23 DoF
human kinematic model and accounts for the presence of a moving collaborator
and obstacles in the environment. Our results suggest that in most cases, our
method outperforms baseline methods when predicting motions. We also show that
our method outperforms baselines for predicting human motion when a human and a
robot share the workspace.Comment: 12 pages, Accepted for publication IEEE Transaction on Robotics 201
Adaptation of sensor morphology: an integrative view of perception from biologically inspired robotics perspective
Sensor morphology, the morphology of a sensing mechanism which plays a role of shaping the desired response from physical stimuli from surroundings to generate signals usable as sensory information, is one of the key common aspects of sensing processes. This paper presents a structured review of researches on bioinspired sensor morphology implemented in robotic systems, and discusses the fundamental design principles. Based on literature review, we propose two key arguments: first, owing to its synthetic nature, biologically inspired robotics approach is a unique and powerful methodology to understand the role of sensor morphology and how it can evolve and adapt to its task and environment. Second, a consideration of an integrative view of perception by looking into multidisciplinary and overarching mechanisms of sensor morphology adaptation across biology and engineering enables us to extract relevant design principles that are important to extend our understanding of the unfinished concepts in sensing and perceptionThis study was supported by the European Commission with the RoboSoft CA (A Coordination Action for Soft Robotics, contract #619319).
SGN was supported by School of Engineering seed funding (2016), Malaysia Campus, Monash University
심층 강화학습을 이용한 사람의 모션을 통한 이형적 캐릭터 제어기 개발
학위논문(석사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2022. 8. 서진욱.사람의 모션을 이용한 로봇 컨트롤 인터페이스는 사용자의 직관과 로봇의 모터 능력을 합하여 위험한 환경에서 로봇의 유연한 작동을 만들어낸다. 하지만, 휴머노이드 외의 사족보행 로봇이나 육족보행 로봇을 위한 모션 인터페이스를 디자인 하는 것은 쉬운일이 아니다. 이것은 사람과 로봇 사이의 형태 차이로 오는 다이나믹스 차이와 제어 전략이 크게 차이나기 때문이다. 우리는 사람 사용자가 움직임을 통하여 사족보행 로봇에서 부드럽게 여러 과제를 수행할 수 있게끔 하는 새로운 모션 제어 시스템을 제안한다. 우리는 우선 캡쳐한 사람의 모션을 상응하는 로봇의 모션으로 리타겟 시킨다. 이때 상응하는 로봇의 모션은 유저가 의도한 의미를 내포하게 되며, 우리는 이를 지도학습 방법과 후처리 기술을 이용하여 가능케 하였다. 그 뒤 우리는 모션을 모사하는 학습을 커리큘럼 학습과 병행하여 주어진 리타겟된 참조 모션을 따라가는 제어 정책을 생성하였다. 우리는 "전문가 집단"을 학습함으로 모션 리타게팅 모듈과 모션 모사 모듈의 성능을 크게 증가시켰다. 결과에서 볼 수 있듯, 우리의 시스템을 이용하여 사용자가 사족보행 로봇의 서있기, 앉기, 기울이기, 팔 뻗기, 걷기, 돌기와 같은 다양한 모터 과제들을 시뮬레이션 환경과 현실에서 둘 다 수행할 수 있었다. 우리는 연구의 성능을 평가하기 위하여 다양한 분석을 하였으며, 특히 우리 시스템의 각각의 요소들의 중요성을 보여줄 수 있는 실험들을 진행하였다.A human motion-based interface fuses operator intuitions with the motor capabilities of robots, enabling adaptable robot operations in dangerous environments. However, the challenge of designing a motion interface for non-humanoid robots, such as quadrupeds or hexapods, is emerged from the different morphology and dynamics of a human controller, leading to an ambiguity of control strategy. We propose a novel control framework that allows human operators to execute various motor skills on a quadrupedal robot by their motion. Our system first retargets the captured human motion into the corresponding robot motion with the operator's intended semantics. The supervised learning and post-processing techniques allow this retargeting skill which is ambiguity-free and suitable for control policy training. To enable a robot to track a given retargeted motion, we then obtain the control policy from reinforcement learning that imitates the given reference motion with designed curriculums. We additionally enhance the system's performance by introducing a set of experts. Finally, we randomize the domain parameters to adapt the physically simulated motor skills to real-world tasks. We demonstrate that a human operator can perform various motor tasks using our system including standing, tilting, manipulating, sitting, walking, and steering on both physically simulated and real quadruped robots. We also analyze the performance of each system component ablation study.1 Introduction 1
2 Related Work 5
2.1 Legged Robot Control 5
2.2 Motion Imitation 6
2.3 Motion-based Control 7
3 Overview 9
4 Motion Retargeting Module 11
4.1 Motion Retargeting Network 12
4.2 Post-processing for Consistency 14
4.3 A Set of Experts for Multi-task Support 15
5 Motion Imitation Module 17
5.1 Background: Reinforcement Learning 18
5.2 Formulation of Motion Imitation 18
5.3 Curriculum Learning over Tasks and Difficulties 21
5.4 Hierarchical Control with States 21
5.5 Domain Randomization 22
6 Results and Analysis 23
6.1 Experimental Setup 23
6.2 Motion Performance 24
6.3 Analysis 28
6.4 Comparison to Other Methods 31
7 Conclusion And Future Work 32
Bibliography 34
Abstract (In Korean) 44
감사의 글 45석
Online Inverse Optimal Control for Control-Constrained Discrete-Time Systems on Finite and Infinite Horizons
In this paper, we consider the problem of computing parameters of an
objective function for a discrete-time optimal control problem from state and
control trajectories with active control constraints. We propose a novel method
of inverse optimal control that has a computationally efficient online form in
which pairs of states and controls from given state and control trajectories
are processed sequentially without being stored or processed in batches. We
establish conditions guaranteeing the uniqueness of the objective-function
parameters computed by our proposed method from trajectories with active
control constraints. We illustrate our proposed method in simulation.Comment: 10 pages, 4 figures, Accepted for publication in Automatic
Human-like arm motion generation: a review
In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most recent research on the generation of human-like arm movements in humanoid and manipulation robotic systems. Search methods and inclusion criteria are described. The studies are analyzed taking into consideration the sources of publication, the experimental settings, the type of movements, the technical approach, and the human motor principles that have been used to inspire and assess human-likeness. Results show that there is a strong focus on the generation of single-arm reaching movements and biomimetic-based methods. However, there has been poor attention to manipulation, obstacle-avoidance mechanisms, and dual-arm motion generation. For these reasons, human-like arm motion generation may not fully respect human behavioral and neurological key features and may result restricted to specific tasks of human-robot interaction. Limitations and challenges are discussed to provide meaningful directions for future investigations.FCT Project UID/MAT/00013/2013FCT–Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020
Modeling human-likeness in approaching motions of dual-arm autonomous robots
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper addresses the problem of obtaining human-like motions with an anthropomorphic dual-arm torso assembled on a mobile platform. The focus is set on the coordinated movements of the robotic arms and the robot base while approaching a table to subsequently perform a bimanual manipulation task. For this, human movements are captured and mapped to the robot in order to compute the human dual-arm synergies. Since the demonstrated synergies change depending on the robot position, a recursive Cartesian-space discretization is presented based on these differences. Thereby, different movements of the arms are assigned to different regions of the Cartesian space. As an application example, a motion-planning algorithm exploiting this information is proposed and used.Postprint (published version
Modeling of human movement for the generation of humanoid robot motion
La robotique humanoïde arrive a maturité avec des robots plus rapides et plus précis. Pour faire face à la complexité mécanique, la recherche a commencé à regarder au-delà du cadre habituel de la robotique, vers les sciences de la vie, afin de mieux organiser le contrôle du mouvement. Cette thèse explore le lien entre mouvement humain et le contrôle des systèmes anthropomorphes tels que les robots humanoïdes. Tout d’abord, en utilisant des méthodes classiques de la robotique, telles que l’optimisation, nous étudions les principes qui sont à la base de mouvements répétitifs humains, tels que ceux effectués lorsqu’on joue au yoyo. Nous nous concentrons ensuite sur la locomotion en nous inspirant de résultats en neurosciences qui mettent en évidence le rôle de la tête dans la marche humaine. En développant une interface permettant à un utilisateur de commander la tête du robot, nous proposons une méthode de contrôle du mouvement corps-complet d’un robot humanoïde, incluant la production de pas et permettant au corps de suivre le mouvement de la tête. Cette idée est poursuivie dans l’étude finale dans laquelle nous analysons la locomotion de sujets humains, dirigée vers une cible, afin d’extraire des caractéristiques du mouvement sous forme invariants. En faisant le lien entre la notion “d’invariant” en neurosciences et celle de “tâche cinématique” en robotique humanoïde, nous développons une méthode pour produire une locomotion réaliste pour d’autres systèmes anthropomorphes. Dans ce cas, les résultats sont illustrés sur le robot humanoïde HRP2 du LAAS-CNRS. La contribution générale de cette thèse est de montrer que, bien que la planification de mouvement pour les robots humanoïdes peut être traitée par des méthodes classiques de robotique, la production de mouvements réalistes nécessite de combiner ces méthodes à l’observation systématique et formelle du comportement humain. ABSTRACT : Humanoid robotics is coming of age with faster and more agile robots. To compliment the physical complexity of humanoid robots, the robotics algorithms being developed to derive their motion have also become progressively complex. The work in this thesis spans across two research fields, human neuroscience and humanoid robotics, and brings some ideas from the former to aid the latter. By exploring the anthropological link between the structure of a human and that of a humanoid robot we aim to guide conventional robotics methods like local optimization and task-based inverse kinematics towards more realistic human-like solutions. First, we look at dynamic manipulation of human hand trajectories while playing with a yoyo. By recording human yoyo playing, we identify the control scheme used as well as a detailed dynamic model of the hand-yoyo system. Using optimization this model is then used to implement stable yoyo-playing within the kinematic and dynamic limits of the humanoid HRP-2. The thesis then extends its focus to human and humanoid locomotion. We take inspiration from human neuroscience research on the role of the head in human walking and implement a humanoid robotics analogy to this. By allowing a user to steer the head of a humanoid, we develop a control method to generate deliberative whole-body humanoid motion including stepping, purely as a consequence of the head movement. This idea of understanding locomotion as a consequence of reaching a goal is extended in the final study where we look at human motion in more detail. Here, we aim to draw to a link between “invariants” in neuroscience and “kinematic tasks” in humanoid robotics. We record and extract stereotypical characteristics of human movements during a walking and grasping task. These results are then normalized and generalized such that they can be regenerated for other anthropomorphic figures with different kinematic limits than that of humans. The final experiments show a generalized stack of tasks that can generate realistic walking and grasping motion for the humanoid HRP-2. The general contribution of this thesis is in showing that while motion planning for humanoid robots can be tackled by classical methods of robotics, the production of realistic movements necessitate the combination of these methods with the systematic and formal observation of human behavior
Motion Synthesis and Control for Autonomous Agents using Generative Models and Reinforcement Learning
Imitating and predicting human motions have wide applications in both graphics and robotics, from developing realistic models of human movement and behavior in immersive virtual worlds and games to improving autonomous navigation for service agents deployed in the real world. Traditional approaches for motion imitation and prediction typically rely on pre-defined rules to model agent behaviors or use reinforcement learning with manually designed reward functions. Despite impressive results, such approaches cannot effectively capture the diversity of motor behaviors and the decision making capabilities of human beings. Furthermore, manually designing a model or reward function to explicitly describe human motion characteristics often involves laborious fine-tuning and repeated experiments, and may suffer from generalization issues. In this thesis, we explore data-driven approaches using generative models and reinforcement learning to study and simulate human motions. Specifically, we begin with motion synthesis and control of physically simulated agents imitating a wide range of human motor skills, and then focus on improving the local navigation decisions of autonomous agents in multi-agent interaction settings. For physics-based agent control, we introduce an imitation learning framework built upon generative adversarial networks and reinforcement learning that enables humanoid agents to learn motor skills from a few examples of human reference motion data. Our approach generates high-fidelity motions and robust controllers without needing to manually design and finetune a reward function, allowing at the same time interactive switching between different controllers based on user input. Based on this framework, we further propose a multi-objective learning scheme for composite and task-driven control of humanoid agents. Our multi-objective learning scheme balances the simultaneous learning of disparate motions from multiple reference sources and multiple goal-directed control objectives in an adaptive way, enabling the training of efficient composite motion controllers. Additionally, we present a general framework for fast and robust learning of motor control skills. Our framework exploits particle filtering to dynamically explore and discretize the high-dimensional action space involved in continuous control tasks, and provides a multi-modal policy as a substitute for the commonly used Gaussian policies. For navigation learning, we leverage human crowd data to train a human-inspired collision avoidance policy by combining knowledge distillation and reinforcement learning. Our approach enables autonomous agents to take human-like actions during goal-directed steering in fully decentralized, multi-agent environments. To inform better control in such environments, we propose SocialVAE, a variational autoencoder based architecture that uses timewise latent variables with socially-aware conditions and a backward posterior approximation to perform agent trajectory prediction. Our approach improves current state-of-the-art performance on trajectory prediction tasks in daily human interaction scenarios and more complex scenes involving interactions between NBA players. We further extend SocialVAE by exploiting semantic maps as context conditions to generate map-compliant trajectory prediction. Our approach processes context conditions and social conditions occurring during agent-agent interactions in an integrated manner through the use of a dual-attention mechanism. We demonstrate the real-time performance of our approach and its ability to provide high-fidelity, multi-modal predictions on various large-scale vehicle trajectory prediction tasks
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