Protosymbols that integrate recognition and response

We explore two controversial hypotheses through robotic implementation: (1) Processes involved in recognition and response are tightly coupled both in their operation and epigenesis; and (2) processes involved in symbol emergence should respect the integrity of recognition and response while exploiting the periodicity of biological motion. To that end, this paper proposes a method of recognizing and generating motion patterns based on nonlinear principal component neural networks that are constrained to model both periodic and transitional movements. The method is evaluated by an examination of its ability to segment and generalize different kinds of soccer playing activity during a RoboCup match

Evolution of Prehension Ability in an Anthropomorphic Neurorobotic Arm

In this paper we show how a simulated anthropomorphic robotic arm controlled by an artificial neural network can develop effective reaching and grasping behaviour through a trial and error process in which the free parameters encode the control rules which regulate the fine-grained interaction between the robot and the environment and variations of the free parameters are retained or discarded on the basis of their effects at the level of the global behaviour exhibited by the robot situated in the environment. The obtained results demonstrate how the proposed methodology allows the robot to produce effective behaviours thanks to its ability to exploit the morphological properties of the robot’s body (i.e. its anthropomorphic shape, the elastic properties of its muscle-like actuators, and the compliance of its actuated joints) and the properties which arise from the physical interaction between the robot and the environment mediated by appropriate control rules

Body models in humans, animals, and robots: mechanisms and plasticity

Humans and animals excel in combining information from multiple sensory modalities, controlling their complex bodies, adapting to growth, failures, or using tools. These capabilities are also highly desirable in robots. They are displayed by machines to some extent - yet, as is so often the case, the artificial creatures are lagging behind. The key foundation is an internal representation of the body that the agent - human, animal, or robot - has developed. In the biological realm, evidence has been accumulated by diverse disciplines giving rise to the concepts of body image, body schema, and others. In robotics, a model of the robot is an indispensable component that enables to control the machine. In this article I compare the character of body representations in biology with their robotic counterparts and relate that to the differences in performance that we observe. I put forth a number of axes regarding the nature of such body models: fixed vs. plastic, amodal vs. modal, explicit vs. implicit, serial vs. parallel, modular vs. holistic, and centralized vs. distributed. An interesting trend emerges: on many of the axes, there is a sequence from robot body models, over body image, body schema, to the body representation in lower animals like the octopus. In some sense, robots have a lot in common with Ian Waterman - "the man who lost his body" - in that they rely on an explicit, veridical body model (body image taken to the extreme) and lack any implicit, multimodal representation (like the body schema) of their bodies. I will then detail how robots can inform the biological sciences dealing with body representations and finally, I will study which of the features of the "body in the brain" should be transferred to robots, giving rise to more adaptive and resilient, self-calibrating machines.Comment: 27 pages, 8 figure

외란 및 토크 대역폭 제한을 고려한 토크 기반의 작업 공간 제어

학위논문(박사) -- 서울대학교대학원 : 융합과학기술대학원 융합과학부(지능형융합시스템전공), 2021.8. 박재흥.The thesis aims to improve the control performance of the torque-based operational space controller under disturbance and torque bandwidth limitation. Torque-based robot controllers command the desired torque as an input signal to the actuator. Since the torque is at force-level, the torque-controlled robot is more compliant to external forces from the environment or people than the position-controlled robot. Therefore, it can be used effectively for the tasks involving contact such as legged locomotion or human-robot interaction. Operational space control strengthens this advantage for redundant robots due to the inherent compliance in the null space of given tasks. However, high-level torque-based controllers have not been widely used for transitional robots such as industrial manipulators due to the low performance of precise control. One of the reasons is the uncertainty or disturbance in the kinematic and dynamic properties of the robot model. It leads to the inaccurate computation of the desired torque, deteriorating the control stability and performance. To estimate and compensate the disturbance using only proprioceptive sensors, the disturbance observer has been developed using inverse dynamics. It requires the joint acceleration information, which is noisy due to the numerical error in the second-order derivative of the joint position. In this work, a contact-consistent disturbance observer for a floating-base robot is proposed. The method uses the fixed contact position of the supporting foot as the kinematic constraints to estimate the joint acceleration error. It is incorporated into the dynamics model to reduce its effect on the disturbance torque solution, by which the observer becomes less dependent on the low-pass filter design. Another reason for the low performance of precise control is torque bandwidth limitation. Torque bandwidth is determined by the relationship between the input torque commanded to the actuator and the torque actually transmitted into the link. It can be regulated by various factors such as inner torque feedback loop, actuator dynamics, and joint elasticity, which deteriorates the control stability and performance. Operational space control is especially prone to this problem, since the limited bandwidth of a single actuator can reduce the performance of all related tasks simultaneously. In this work, an intuitive way to penalize low performance actuators is proposed for the operational space controller. The basic concept is to add joint torques only to high performance actuators recursively, which has the physical meaning of the joint-weighted torque solution considering each actuator performance. By penalizing the low performance actuators, the torque transmission error is reduced and the task performance is significantly improved. In addition, the joint trajectory is not required, which allows compliance in redundancy. The results of the thesis were verified by experiments using the 12-DOF biped robot DYROS-RED and the 7-DOF robot manipulator Franka Emika Panda.본 학위 논문은 외란과 토크 대역폭 제한이 존재할 때 토크 기반 작업 공간 제어기의 제어 성능을 높이는 것을 목표로 한다. 토크 기반의 로봇 제어기는 목표 토크를 입력 신호로서 구동기에 전달한다. 토크는 힘 레벨이기 때문에, 토크 제어 로봇은 위치 제어 로봇에 비해 외부 환경이나 사람으로부터 가해지는 외력에 더 유연하게 대응할 수 있다. 그러므로 토크 제어는 보행이나 인간-로봇 상호작용과 같은 접촉을 포함하는 작업을 위해 효과적으로 사용될 수 있다. 작업 공간 제어는 이러한 토크 제어의 장점을 더 강화시킬 수 있는데, 로봇이 여유 자유도가 있을 때 작업의 영공간에서 존재하는 모션들이 내재적으로 유연하기 때문이다. 그러나 이러한 장점에도 불구하고 토크 기반의 로봇 제어기는 정밀 제어 성능이 떨어지기 때문에 산업용 로봇 팔과 같은 전통적인 로봇에는 널리 사용되지 못했다. 그 이유 중 한 가지는 로봇 모델의 기구학 및 동역학 물성치에 존재하는 외란이다. 모델 오차는 목표 토크를 계산할 때 오차를 유발하며, 이것이 제어 안정성과 성능을 약화시키게 된다. 외란을 내재 센서만을 이용하여 추정 및 보상하기 위해 역동역학 기반의 외란 관측기가 개발되어 왔다. 외란 관측기는 역동역학 계산을 위해 관절 각가속도 정보가 필요한데, 이 값이 관절 위치를 두 번 미분한 값이기 때문에 수치적인 오차로 노이즈해지는 문제가 있었다. 본 연구에서는 부유형 기저 로봇을 위한 접촉 조건이 고려된 외란 관측기가 제안되었다. 제안된 방법은 로봇의 고정된 접촉 지점에 대한 기구학적인 구속 조건을 이용하여 관절 각가속도 오차를 추정한다. 추정된 오차를 동역학 모델에 반영하여 외란 토크를 계산함으로써 저역 통과 필터 성능에 대한 의존도를 줄일 수 있다. 토크 기반 제어의 정밀 제어 성능이 떨어지는 또 다른 이유 중 하나는 토크 대역폭 제한이다. 토크 대역폭은 구동기에 전달되는 입력 토크와 실제 링크에 전달되는 토크와의 관계로 결정된다. 토크 대역폭은 구동기 내부의 토크 피드백 루프, 구동기 동역학, 관절 탄성 등의 요인들에 의해 제한될 수 있는데 이것이 제어 안정성 및 성능을 감소시킨다. 작업 공간 제어는 특히 이 문제에 취약한데, 대역폭이 제한된 구동기 하나가 그와 연관된 모든 작업 공간의 제어 성능을 감소시킬 수 있기 때문이다. 본 연구에서는 작업 공간 제어기에서 성능이 낮은 구동기의 사용을 제한하기 위한 직관적인 전략이 제안되었다. 기본 컨셉은 작업 제어를 위한 토크 솔루션에 성능이 좋은 관절에만 추가적으로 토크 솔루션을 더해나가는 것으로, 이것은 각 관절의 가중치가 고려된 토크 솔루션이 되는 것을 의미한다. 성능이 낮은 구동기의 사용을 제한함으로써 토크 전달 오차가 줄어들고 작업 성능이 크게 향상될 수 있다. 본 학위 논문의 연구 결과들은 12자유도 이족 보행 로봇 DYROS-RED와 7자유도 로봇 팔 Franka Emika Panda를 이용한 실험을 통해 검증되었다.1 INTRODUCTION 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Contributions of Thesis . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Overview of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 BACKGROUNDS 6 2.1 Operational Space Control . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Dynamics Formulation . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.1 Fixed-Base Dynamics . . . . . . . . . . . . . . . . . . . . 9 2.2.1.1 Joint Space Formulation . . . . . . . . . . . . . 9 2.2.1.2 Operational Space Formulation . . . . . . . . . . 11 2.2.2 Floating-Base Dynamics . . . . . . . . . . . . . . . . . . . 12 2.2.2.1 Joint Space Formulation . . . . . . . . . . . . . 12 2.2.2.2 Operational Space Formulation . . . . . . . . . . 14 2.3 Position Tracking via PD Control . . . . . . . . . . . . . . . . . . 17 2.3.1 Torque Solution . . . . . . . . . . . . . . . . . . . . . . . 17 2.3.2 Orientation Control . . . . . . . . . . . . . . . . . . . . . 19 3 CONTACT-CONSISTENT DISTURBANCE OBSERVER FOR FLOATING-BASE ROBOTS 22 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.2 Momentum-Based Disturbance Observer . . . . . . . . . . . . . . 24 3.3 The Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . 25 3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4.2 External Force Estimation . . . . . . . . . . . . . . . . . . 33 3.4.3 Internal Disturbance Rejection . . . . . . . . . . . . . . . 35 3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4 OPERATIONAL SPACE CONTROL UNDER ACTUATOR BANDWIDTH LIMITATION 40 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.2 The Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . 43 4.2.1 General Concepts . . . . . . . . . . . . . . . . . . . . . . . 43 4.2.2 OSF-Based Torque Solution . . . . . . . . . . . . . . . . . 45 4.2.3 Comparison With a Typical Method . . . . . . . . . . . . 47 4.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.4 Comparison With Other Approaches . . . . . . . . . . . . . . . . 61 4.4.1 Controller Formulation . . . . . . . . . . . . . . . . . . . . 62 4.4.1.1 The Proposed Method . . . . . . . . . . . . . . . 62 4.4.1.2 The OSF Controller . . . . . . . . . . . . . . . . 62 4.4.1.3 The OSF-Filter Controller . . . . . . . . . . . . 62 4.4.1.4 The OSF-Joint Controller . . . . . . . . . . . . . 67 4.4.1.5 The Joint Controller . . . . . . . . . . . . . . . . 68 4.4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.5 Frequency Response of Joint Torque . . . . . . . . . . . . . . . . 72 4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 5 CONCLUSION 85 Abstract (In Korean) 100박

Evolution of Grasping Behaviour in Anthropomorphic Robotic Arms with Embodied Neural Controllers

The works reported in this thesis focus upon synthesising neural controllers for anthropomorphic robots that are able to manipulate objects through an automatic design process based on artificial evolution. The use of Evolutionary Robotics makes it possible to reduce the characteristics and parameters specified by the designer to a minimum, and the robot’s skills evolve as it interacts with the environment. The primary objective of these experiments is to investigate whether neural controllers that are regulating the state of the motors on the basis of the current and previously experienced sensors (i.e. without relying on an inverse model) can enable the robots to solve such complex tasks. Another objective of these experiments is to investigate whether the Evolutionary Robotics approach can be successfully applied to scenarios that are significantly more complex than those to which it is typically applied (in terms of the complexity of the robot’s morphology, the size of the neural controller, and the complexity of the task). The obtained results indicate that skills such as reaching, grasping, and discriminating among objects can be accomplished without the need to learn precise inverse internal models of the arm/hand structure. This would also support the hypothesis that the human central nervous system (cns) does necessarily have internal models of the limbs (not excluding the fact that it might possess such models for other purposes), but can act by shifting the equilibrium points/cycles of the underlying musculoskeletal system. Consequently, the resulting controllers of such fundamental skills would be less complex. Thus, the learning of more complex behaviours will be easier to design because the underlying controller of the arm/hand structure is less complex. Moreover, the obtained results also show how evolved robots exploit sensory-motor coordination in order to accomplish their tasks

Evolution of Prehension Ability in an Anthropomorphic Neurorobotic Arm

In this paper, we show how a simulated anthropomorphic robotic arm controlled by an artificial neural network can develop effective reaching and grasping behaviour through a trial and error process in which the free parameters encode the control rules which regulate the fine-grained interaction between the robot and the environment and variations of the free parameters are retained or discarded on the basis of their effects at the level of the global behaviour exhibited by the robot situated in the environment. The obtained results demonstrate how the proposed methodology allows the robot to produce effective behaviours thanks to its ability to exploit the morphological properties of the robot's body (i.e. its anthropomorphic shape, the elastic properties of its muscle-like actuators and the compliance of its actuated joints) and the properties which arise from the physical interaction between the robot and the environment mediated by appropriate control rules

Learning to reach and reaching to learn: a unified approach to path planning and reactive control through reinforcement learning

The next generation of intelligent robots will need to be able to plan reaches. Not just ballistic point to point reaches, but reaches around things such as the edge of a table, a nearby human, or any other known object in the robot’s workspace. Planning reaches may seem easy to us humans, because we do it so intuitively, but it has proven to be a challenging problem, which continues to limit the versatility of what robots can do today. In this document, I propose a novel intrinsically motivated RL system that draws on both Path/Motion Planning and Reactive Control. Through Reinforcement Learning, it tightly integrates these two previously disparate approaches to robotics. The RL system is evaluated on a task, which is as yet unsolved by roboticists in practice. That is to put the palm of the iCub humanoid robot on arbitrary target objects in its workspace, start- ing from arbitrary initial configurations. Such motions can be generated by planning, or searching the configuration space, but this typically results in some kind of trajectory, which must then be tracked by a separate controller, and such an approach offers a brit- tle runtime solution because it is inflexible. Purely reactive systems are robust to many problems that render a planned trajectory infeasible, but lacking the capacity to search, they tend to get stuck behind constraints, and therefore do not replace motion planners. The planner/controller proposed here is novel in that it deliberately plans reaches without the need to track trajectories. Instead, reaches are composed of sequences of reactive motion primitives, implemented by my Modular Behavioral Environment (MoBeE), which provides (fictitious) force control with reactive collision avoidance by way of a realtime kinematic/geometric model of the robot and its workspace. Thus, to the best of my knowledge, mine is the first reach planning approach to simultaneously offer the best of both the Path/Motion Planning and Reactive Control approaches. By controlling the real, physical robot directly, and feeling the influence of the con- straints imposed by MoBeE, the proposed system learns a stochastic model of the iCub’s configuration space. Then, the model is exploited as a multiple query path planner to find sensible pre-reach poses, from which to initiate reaching actions. Experiments show that the system can autonomously find practical reaches to target objects in workspace and offers excellent robustness to changes in the workspace configuration as well as noise in the robot’s sensory-motor apparatus

Ecological active vision: four bio-inspired principles to integrate bottom-up and adaptive top-down attention tested with a simple camera-arm robot

Vision gives primates a wealth of information useful to manipulate the environment, but at the same time it can easily overwhelm their computational resources. Active vision is a key solution found by nature to solve this problem: a limited fovea actively displaced in space to collect only relevant information. Here we highlight that in ecological conditions this solution encounters four problems: 1) the agent needs to learn where to look based on its goals; 2) manipulation causes learning feedback in areas of space possibly outside the attention focus; 3) good visual actions are needed to guide manipulation actions, but only these can generate learning feedback; and 4) a limited fovea causes aliasing problems. We then propose a computational architecture ("BITPIC") to overcome the four problems, integrating four bioinspired key ingredients: 1) reinforcement-learning fovea-based top-down attention; 2) a strong vision-manipulation coupling; 3) bottom-up periphery-based attention; and 4) a novel action-oriented memory. The system is tested with a simple simulated camera-arm robot solving a class of search-and-reach tasks involving color-blob "objects." The results show that the architecture solves the problems, and hence the tasks, very ef?ciently, and highlight how the architecture principles can contribute to a full exploitation of the advantages of active vision in ecological conditions

A Motion Planning Approach to Automatic Obstacle Avoidance during Concentric Tube Robot Teleoperation

Abstract-Concentric tube robots are thin, tentacle-like devices that can move along curved paths and can potentially enable new, less invasive surgical procedures. Safe and effective operation of this type of robot requires that the robot's shaft avoid sensitive anatomical structures (e.g., critical vessels and organs) while the surgeon teleoperates the robot's tip. However, the robot's unintuitive kinematics makes it difficult for a human user to manually ensure obstacle avoidance along the entire tentacle-like shape of the robot's shaft. We present a motion planning approach for concentric tube robot teleoperation that enables the robot to interactively maneuver its tip to points selected by a user while automatically avoiding obstacles along its shaft. We achieve automatic collision avoidance by precomputing a roadmap of collision-free robot configurations based on a description of the anatomical obstacles, which are attainable via volumetric medical imaging. We also mitigate the effects of kinematic modeling error in reaching the goal positions by adjusting motions based on robot tip position sensing. We evaluate our motion planner on a teleoperated concentric tube robot and demonstrate its obstacle avoidance and accuracy in environments with tubular obstacles