Benchmarking Deep Reinforcement Learning for Continuous Control

Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning. Some notable examples include training agents to play Atari games based on raw pixel data and to acquire advanced manipulation skills using raw sensory inputs. However, it has been difficult to quantify progress in the domain of continuous control due to the lack of a commonly adopted benchmark. In this work, we present a benchmark suite of continuous control tasks, including classic tasks like cart-pole swing-up, tasks with very high state and action dimensionality such as 3D humanoid locomotion, tasks with partial observations, and tasks with hierarchical structure. We report novel findings based on the systematic evaluation of a range of implemented reinforcement learning algorithms. Both the benchmark and reference implementations are released at https://github.com/rllab/rllab in order to facilitate experimental reproducibility and to encourage adoption by other researchers.Comment: 14 pages, ICML 201

3LP: a linear 3D-walking model including torso and swing dynamics

In this paper, we present a new model of biped locomotion which is composed of three linear pendulums (one per leg and one for the whole upper body) to describe stance, swing and torso dynamics. In addition to double support, this model has different actuation possibilities in the swing hip and stance ankle which could be widely used to produce different walking gaits. Without the need for numerical time-integration, closed-form solutions help finding periodic gaits which could be simply scaled in certain dimensions to modulate the motion online. Thanks to linearity properties, the proposed model can provide a computationally fast platform for model predictive controllers to predict the future and consider meaningful inequality constraints to ensure feasibility of the motion. Such property is coming from describing dynamics with joint torques directly and therefore, reflecting hardware limitations more precisely, even in the very abstract high level template space. The proposed model produces human-like torque and ground reaction force profiles and thus, compared to point-mass models, it is more promising for precise control of humanoid robots. Despite being linear and lacking many other features of human walking like CoM excursion, knee flexion and ground clearance, we show that the proposed model can predict one of the main optimality trends in human walking, i.e. nonlinear speed-frequency relationship. In this paper, we mainly focus on describing the model and its capabilities, comparing it with human data and calculating optimal human gait variables. Setting up control problems and advanced biomechanical analysis still remain for future works.Comment: Journal paper under revie

Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics

Recently several hierarchical inverse dynamics controllers based on cascades of quadratic programs have been proposed for application on torque controlled robots. They have important theoretical benefits but have never been implemented on a torque controlled robot where model inaccuracies and real-time computation requirements can be problematic. In this contribution we present an experimental evaluation of these algorithms in the context of balance control for a humanoid robot. The presented experiments demonstrate the applicability of the approach under real robot conditions (i.e. model uncertainty, estimation errors, etc). We propose a simplification of the optimization problem that allows us to decrease computation time enough to implement it in a fast torque control loop. We implement a momentum-based balance controller which shows robust performance in face of unknown disturbances, even when the robot is standing on only one foot. In a second experiment, a tracking task is evaluated to demonstrate the performance of the controller with more complicated hierarchies. Our results show that hierarchical inverse dynamics controllers can be used for feedback control of humanoid robots and that momentum-based balance control can be efficiently implemented on a real robot.Comment: appears in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 201

Momentum Control with Hierarchical Inverse Dynamics on a Torque-Controlled Humanoid

Hierarchical inverse dynamics based on cascades of quadratic programs have been proposed for the control of legged robots. They have important benefits but to the best of our knowledge have never been implemented on a torque controlled humanoid where model inaccuracies, sensor noise and real-time computation requirements can be problematic. Using a reformulation of existing algorithms, we propose a simplification of the problem that allows to achieve real-time control. Momentum-based control is integrated in the task hierarchy and a LQR design approach is used to compute the desired associated closed-loop behavior and improve performance. Extensive experiments on various balancing and tracking tasks show very robust performance in the face of unknown disturbances, even when the humanoid is standing on one foot. Our results demonstrate that hierarchical inverse dynamics together with momentum control can be efficiently used for feedback control under real robot conditions.Comment: 21 pages, 11 figures, 4 tables in Autonomous Robots (2015

High-performance series elastic actuation

textMobile legged robots have the potential to restructure many aspects of our lives in the near future. Whether for applications in household care, entertainment, or disaster response, these systems depend on high-performance actuators to improve their basic capabilities. The work presented here focuses on developing new high-performance actuators, specifically series elastic actuators, to address this need. We adopt a system-wide optimization approach, dealing with factors which influence performance at the levels of mechanical design, electrical system design, and control. Using this approach and based on a set of performance metrics, we produce an actuator, the UT-SEA, which achieves leading empirical results in terms of power-to-weight, force control, size, and system efficiency. We also develop general high-performance control techniques for both force- and position-controlled actuators, some of which were adopted for use on NASA-JSC's Valkyrie Humanoid robot and were used during DARPA's DRC Trials 2013 robotics competition.Electrical and Computer Engineerin

Modification of Gesture-Determined-Dynamic Function with Consideration of Margins for Motion Planning of Humanoid Robots

The gesture-determined-dynamic function (GDDF) offers an effective way to handle the control problems of humanoid robots. Specifically, GDDF is utilized to constrain the movements of dual arms of humanoid robots and steer specific gestures to conduct demanding tasks under certain conditions. However, there is still a deficiency in this scheme. Through experiments, we found that the joints of the dual arms, which can be regarded as the redundant manipulators, could exceed their limits slightly at the joint angle level. The performance straightly depends on the parameters designed beforehand for the GDDF, which causes a lack of adaptability to the practical applications of this method. In this paper, a modified scheme of GDDF with consideration of margins (MGDDF) is proposed. This MGDDF scheme is based on quadratic programming (QP) framework, which is widely applied to solving the redundancy resolution problems of robot arms. Moreover, three margins are introduced in the proposed MGDDF scheme to avoid joint limits. With consideration of these margins, the joints of manipulators of the humanoid robots will not exceed their limits, and the potential damages which might be caused by exceeding limits will be completely avoided. Computer simulations conducted on MATLAB further verify the feasibility and superiority of the proposed MGDDF scheme

구조로봇을 위한 강건한 계층적 동작 계획 및 제어

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2021.8. 박종우.Over the last several years, robotics has experienced a striking development, and a new generation of robots has emerged that shows great promise in being able to accomplish complex tasks associated with human behavior. Nowadays the objectives of the robots are no longer restricted to the automaton in the industrial process but are changing into explorers for hazardous, harsh, uncooperative, and extreme environments. As these robots usually operate in dynamic and unstructured environments, they should be robust, adaptive, and reactive under various changing operation conditions. We propose online hierarchical optimization-based planning and control methodologies for a rescue robot to execute a given mission in such a highly unstructured environment. A large number of degrees of freedom is provided to robots in order to achieve diverse kinematic and dynamic tasks. However, accomplishing such multiple objectives renders on-line reactive motion planning and control problems more difficult to solve due to the incompatible tasks. To address this problem, we exploit a hierarchical structure to precisely resolve conflicts by creating a priority in which every task is achieved as much as possible according to the levels. In particular, we concentrate on the reasoning about the task regularization to ensure the convergence and robustness of a solution in the face of singularity. As robotic systems with real-time motion planners or controllers often execute unrehearsed missions, a desired task cannot always be driven to a singularity free configuration. We develop a generic solver for regularized hierarchical quadratic programming without resorting to any off-the-shelf QP solver to take advantage of the null-space projections for computational efficiency. Therefore, the underlying principles are thoroughly investigated. The robust optimal solution is obtained under both equality and inequality tasks or constraints while addressing all problems resulting from the regularization. Especially as a singular value decomposition centric approach is leveraged, all hierarchical solutions and Lagrange multipliers for properly handling the inequality constraints are analytically acquired in a recursive procedure. The proposed algorithm works fast enough to be used as a practical means of real-time control system, so that it can be used for online motion planning, motion control, and interaction force control in a single hierarchical optimization. Core system design concepts of the rescue robot are presented. The goals of the robot are to safely extract a patient and to dispose a dangerous object instead of humans. The upper body is designed humanoid in form with replaceable modularized dual arms. The lower body is featured with a hybrid tracked and legged mobile platform to simultaneously acquire versatile manipulability and all-terrain mobility. Thus, the robot can successfully execute a driving task, dangerous object manipulation, and casualty extraction missions by changing the pose and modularized equipments in an optimized manner. Throughout the dissertation, all proposed methods are validated through extensive numerical simulations and experimental tests. We highlight precisely how the rescue robot can execute a casualty extraction and a dangerous object disposal mission both in indoor and outdoor environments that none of the existing robots has performed.최근에 등장한 새로운 세대의 로봇은 기존에는 인간만이 할 수 있었던 복잡한 일을 로봇 또한 수행할 수 있음을 보여주었다. 특히 DARPA Robotics Challenge를 통해 이러한 사실을 잘 확인할 수 있으며, 이 로봇들은 공장과 같은 정형화된 환경에서 자동화된 일을 반복적으로 수행하던 임무에서 더 나아가 극한의 환경에서 인간을 대신하여 위험한 임무를 수행할 수 있는 방향으로 발전하고 있다. 그래서 사람들은 재난환경에서 안전하고 시의 적절하게 대응할 수 있는 여러 가지 대안 중에서 실현 가능성이 높은 대처 방안으로 로봇을 생각하게 되었다. 하지만 이러한 로봇은 동적으로 변화하는 비정형 환경에서 임무를 수행할 수 있어야 하기 때문에 불확실성에 대해 강건해야하고, 다양한 환경 조건에서 능동적으로 반응을 할 수 있어야 한다. 본 학위논문에서는 로봇이 비정형 환경에서 강건하면서도 적응적으로 동작할 수 있는 실시간 최적화 기반의 동작 계획 및 제어 방법과 구조 로봇의 설계 개념을 제안하고자 한다. 인간은 많은 자유도를 가지고 있으며, 하나의 전신 동작을 생성할 때 다양한 기구학 혹은 동역학적 특성을 가지는 세부 동작 혹은 작업을 정의하고, 이를 효과적으로 종합할 수 있다. 그리고 학습을 통해 각 동작 요소들을 최적화할 뿐만 아니라 상황 에 따라 각 동작 요소에 우선순위를 부여하여 이를 효과적으로 결합하거나 분리하여 실시간으로 최적의 동작을 생성하고 제어한다. 즉, 상황에 따라 중요한 동작요소를 우선적으로 수행하고 우선순위가 낮은 동작요소는 부분 혹은 전체적으로 포기하기도 하면서 매우 유연하게 전체 동작을 생성하고 최적화 한다. 인간과 같이 다자유도를 보유한 로봇 또한 기구학과 동역학적 특성을 가지는 다양한 세부 동작 혹은 작업을 작업공간(task space) 혹은 관절공간(configuration space)에서 정의할 수 있으며, 우선순위에 따라 이를 효과적으로 결합하여 전체 동작을 생 성하고 제어할 수 있다. 서로 양립하기 어려운 로봇의 동작 문제를 해결하기 위해 동작들 사이에 우선순위를 부여하여 계층을 생성하고, 이에 따라 로봇의 전신 동작을 구현하는 방법은 오랫동안 연구가 진행되어 왔다. 이러한 계층적 최적화를 이용하면 우선순위가 높은 동작부터 순차적으로 실행하지만, 우선순위가 낮은 동작요소들도 가능한 만족시키는 최적의 해를 찾을 수 있다. 하지만 관절의 구동 범위와 같은 부등식의 조건이 포함된 계층적 최적화 문제에서 특이점에 대한 강건성까지 확보할 수 있는 방법에 대해서는 아직까지 많은 부분이 밝 혀진 바가 없다. 따라서 본 학위논문에서는 등식과 부등식으로 표현되는 구속조건 혹은 동작요소를 계층적 최적화에 동시에 포함시키고, 특이점이 존재하더라도 강건성과 수렴성을 보장하는 관절공간에서의 최적해를 확보하는데 집중한다. 왜나하면 비정형 임무를 수행하는 로봇은 사전에 계획된 동작을 수행하는 것이 아닌 변화하는 환경조건에 따라 실시간으로 동작을 계획하고 제어해야 하기 때문에 특이점이 없는 자세로 로봇을 항상 제어하기가 어렵다. 그리고 이렇게 특이점을 회피하는 방향으로 로봇을 제어하는 것은 로봇의 운용성을 심각하게 저해시킬 수 있다. 특이점 근방에서의 해의 강건성이 보장되지 않으면 로봇 관절에 과도한 속도 혹은 토크가 발생하여 로봇의 임무 수행이 불가능하거나 환경과 로봇의 손상을 초래할 수 있으며, 나아가 로봇과 함께 임무를 수행하는 사람에게 상해를 가할 수도 있다. 특이점에 대한 강건성을 확보하기 위해 우선순위 기반의 계층적 최적화와 정규화 (regularization)를 통합하여 정규화된 계층적 최적화 (RHQP: Regularized Hierarchical Quadratic Program) 문제를 다룬다. 부등식이 포함된 계층적 최적화에 정규화를 동시에 고려함으로써 야기되는 많은 문제점들을 해결하고 해의 최적성과 강건성을 확보할 수 있는 방법을 제안한다. 특히 외부의 최적화 프로그램을 사용하지 않고 수치적 최적화 (numerical optimization) 이론과 우선순위에 기반을 두는 여유자유도 로봇의 해석 기법을 이용하여 계산의 효율성을 극대화할 수 있는 이차 프로그램(quadratic programming)을 제안한다. 또한 이와 동시에 정규화된 계층적 최적화 문제의 이론적 구조를 철저하게 분석한다. 특히 특이값 분해 (singular value decomposition)를 통해 최적해와 부등식 조건을 처리하는데 필요한 라그랑지 승수를 재귀적인 방법으로 해석적 형태로 구함으로써 계산의 효율성을 증대시키고 동시에 부등식의 조건을 오류 없이 정확하게 처리할 수 있도록 하였다. 그리고 정규화된 계층적 최적화를 힘제어까지 확장하여 환경과 로봇의 안전한 상호작용을 보장하여 로봇이 적절한 힘으로 환경과 접촉할 수 있도록 하였다. 불확실성이 존재하는 비정형 환경에서 비정형 임무를 수행할 수 있는 구조로봇의 핵심 설계 개념을 제시한다. 비정형 환경에서의 조작 성능과 이동 성능을 동시에 확보할 수 있는 형상으로 로봇을 설계하여 구조 로봇으로 하여금 최종 목적으로 설정된 인간을 대신하여 부상자를 구조하고 위험물을 처리하는 임무를 효과적으로 수행할 수 있도록 한다. 구조 로봇에 필요한 매니퓰레이터는 부상자 구조 임무와 위험물 처리 임무에 따라 교체 가능한 모듈형으로 설계하여 각각의 임무에 따라 최적화된 매니퓰 레이터를 장착하여 임무를 수행할 수 있다. 하체는 트랙과 관절이 결합된 하이브리드 형태를 취하고 있으며, 주행 임무와 조작임무에 따라 형상을 변경할 수 있다. 형상 변경과 모듈화된 매니퓰레이터를 통해서조작 성능과 험한 지형에서 이동할 수 있는 주행 성능을 동시에 확보하였다. 최종적으로 구조로봇의 설계와 실시간 계층적 제어를 이용하여 비정형 실내외 환경에서 구조로봇이 주행임무, 위험물 조작임무, 부상자 구조 임무를 성공적으로 수 행할 수 있음을 해석과 실험을 통하여 입증함으로써 본 학위논문에서 제안한 설계와 정규화된 계층적 최적화 기반의 제어 전략의 유용성을 검증하였다.1 Introduction 1 1.1 Motivations 1 1.2 Related Works and Research Problems for Hierarchical Control 3 1.2.1 Classical Approaches 3 1.2.2 State-of-the-Art Strategies 4 1.2.3 Research Problems 7 1.3 Robust Rescue Robots 9 1.4 Research Goals 12 1.5 Contributions of ThisThesis 13 1.5.1 Robust Hierarchical Task-Priority Control 13 1.5.2 Design Concepts of Robust Rescue Robot 16 1.5.3 Hierarchical Motion and ForceControl 17 1.6 Dissertation Preview 18 2 Preliminaries for Task-Priority Control Framework 21 2.1 Introduction 21 2.2 Task-Priority Inverse Kinematics 23 2.3 Recursive Formulation of Null Space Projector 28 2.4 Conclusion 31 3 Robust Hierarchical Task-Priority Control 33 3.1 Introduction 33 3.1.1 Motivations 35 3.1.2 Objectives 36 3.2 Task Function Approach 37 3.3 Regularized Hierarchical Optimization with Equality Tasks 41 3.3.1 Regularized Hierarchical Optimization 41 3.3.2 Optimal Solution 45 3.3.3 Task Error and Hierarchical Matrix Decomposition 49 3.3.4 Illustrative Examples for Regularized Hierarchical Optimization 56 3.4 Regularized Hierarchical Optimization with Inequality Constraints 60 3.4.1 Lagrange Multipliers 61 3.4.2 Modified Active Set Method 66 3.4.3 Illustrative Examples of Modified Active Set Method 70 3.4.4 Examples for Hierarchical Optimization with Inequality Constraint 72 3.5 DLS-HQP Algorithm 79 3.6 Concluding Remarks 80 4 Rescue Robot Design and Experimental Results 83 4.1 Introduction 83 4.2 Rescue Robot Design 85 4.2.1 System Design 86 4.2.2 Variable Configuration Mobile Platform 92 4.2.3 Dual Arm Manipulators 95 4.2.4 Software Architecture 97 4.3 Performance Verification for Hierarchical Motion Control 99 4.3.1 Real-Time Motion Generation 99 4.3.2 Task Specifications 103 4.3.3 Singularity Robust Task Priority 106 4.3.4 Inequality Constraint Handling and Computation Time 111 4.4 Singularity Robustness and Inequality Handling for Rescue Mission 117 4.5 Field Tests 122 4.6 Concluding Remarks 126 5 Hierarchical Motion and Force Control 129 5.1 Introduction 129 5.2 Operational Space Control 132 5.3 Acceleration-Based Hierarchical Motion Control 134 5.4 Force Control 137 5.4.1 Force Control with Inner Position Loop 141 5.4.2 Force Control with Inner Velocity Loop 144 5.5 Motion and Force Control 145 5.6 Numerical Results for Acceleration-Based Motion and Force Control 148 5.6.1 Task Specifications 150 5.6.2 Force Control Performance 151 5.6.3 Singularity Robustness and Inequality Constraint Handling 155 5.7 Velocity Resolved Motion and Force Control 160 5.7.1 Velocity-Based Motion and Force Control 161 5.7.2 Experimental Results 163 5.8 Concluding Remarks 167 6 Conclusion 169 6.1 Summary 169 6.2 Concluding Remarks 173 A Appendix 175 A.1 Introduction to PID Control 175 A.2 Inverse Optimal Control 176 A.3 Experimental Results and Conclusion 181 Bibliography 183 Abstract 207박