Bio­-inspired approaches to the control and modelling of an anthropomimetic robot

Introducing robots into human environments requires them to handle settings designed specifically for human size and morphology, however, large, conventional humanoid robots with stiff, high powered joint actuators pose a significant danger to humans. By contrast, “anthropomimetic” robots mimic both human morphology and internal structure; skeleton, muscles, compliance and high redundancy. Although far safer, their resultant compliant structure presents a formidable challenge to conventional control. Here we review, and seek to address, characteristic control issues of this class of robot, whilst exploiting their biomimetic nature by drawing upon biological motor control research. We derive a novel learning controller for discovering effective reaching actions created through sustained activation of one or more muscle synergies, an approach which draws upon strong, recent evidence from animal and humans studies, but is almost unexplored to date in musculoskeletal robot literature. Since the best synergies for a given robot will be unknown, we derive a deliberately simple reinforcement learning approach intended to allow their emergence, in particular those patterns which aid linearization of control. We also draw upon optimal control theories to encourage the emergence of smoother movement by incorporating signal dependent noise and trial repetition. In addition, we argue the utility of developing a detailed dynamic model of a complete robot and present a stable, physics-­‐‑based model, of the anthropomimetic ECCERobot, running in real time with 55 muscles and 88 degrees of freedom. Using the model, we find that effective reaching actions can be learned which employ only two sequential motor co-­‐‑activation patterns, each controlled by just a single common driving signal. Factor analysis shows the emergent muscle co-­‐‑activations can be reconstructed to significant accuracy using weighted combinations of only 13 common fragments, labelled “candidate synergies”. Using these synergies as drivable units the same controller learns the same task both faster and better, however, other reaching tasks perform less well, proportional to dissimilarity; we therefore propose that modifications enabling emergence of a more generic set of synergies are required. Finally, we propose a continuous controller for the robot, based on model predictive control, incorporating our model as a predictive component for state estimation, delay-­‐‑ compensation and planning, including merging of the robot and sensed environment into a single model. We test the delay compensation mechanism by controlling a second copy of the model acting as a proxy for the real robot, finding that performance is significantly improved if a precise degree of compensation is applied and show how rapidly an un-­‐‑compensated controller fails as the model accuracy degrades

Biologically inspired control and modeling of (bio)robotic systems and some applications of fractional calculus in mechanics

U ovom radu, prezentovane su primene biološki inspirisanog modeliranja i upravljanja (bio)mehaničkim (ne)redundantnim mehanizmima, kao i novodobijeni rezultati autora u oblasti primenjene mehanike koji su zasnovani na primeni računa necelobrojnog reda. Prvo, predloženo je korišćenje biološkog analogona-sinergije zahvaljujući postojanju nepromenljivih odlika u izvršavanju funkcionalnih pokreta. Drugo, model (bio)mehaničkog sistema može se dobiti primenom drugog biološkog koncepta poznatim pod nazivom distribuirano pozicioniranje (DP), koji je zasnovan na inercijalnim svojstva i pokretanju zglobova razmatranog mehaničkog sistema. Takođe,predlaže se korišćenje drugih bioloških principa kao što su: princip minimalne interakcije, koji ima glavnu ulogu u hijerarhijskoj strukturi upravljanja i princip samopodešavanja (uvodi lokalne pozitivnu/negativnu povratnu spregu u upravljačkoj petlji i to sa velikim pojačanjem), koji omogućava efikasno ostvarivanje upravljanja na bazi iterativnog prirodnog učenja. Takođe, novi, nedavno publikovani rezultati autora su takođe predstavljeni u oblasti stabilnosti, elektro-viskoelastičnosti i teoriji upravljanja a koji su zasnovani na korišćenju računa necelobrojnog reda.In this paper, the applications of biologically inspired modeling and control of (bio)mechanical (non)redundant mechanisms are presented, as well as newly obtained results of author in mechanics which are based on using fractional calculus. First, it is proposed to use biological analog-synergy due to existence of invariant features in the execution of functional motion. Second, the model of (bio)mechanical system may be obtained using another biological concept called distributed positioning (DP), which is based on the inertial properties and actuation of joints of considered mechanical system. In addition, it is proposed to use other biological principles such as: principle of minimum interaction, which takes a main role in hierarchical structure of control and self-adjusting principle (introduce local positive/negative feedback on control with great amplifying), which allows efficiently realization of control based on iterative natural learning. Also, new, recently obtained results of the author in the fields of stability, electroviscoelasticity, and control theory are presented which are based on using fractional calculus (FC)

Human and robot arm control using the minimum variance principle

Many computational models of human upper limb movement successfully capture some features of human movement, but often lack a compelling biological basis. One that provides such a basis is Harris and Wolpert’s minimum variance model. In this model, the variance of the hand at the end of a movement is minimised, given that the controlling signal is subject to random noise with zero mean and standard deviation proportional to the signal’s amplitude. This criterion offers a consistent explanation for several movement characteristics. This work formulates the minimum variance model into a form suitable for controlling a robot arm. This implementation allows examination of the model properties, specifically its applicability to producing human-like movement. The model is subsequently tested in areas important to studies of human movement and robotics, including reaching, grasping, and action perception. For reaching, experiments show this formulation successfully captures the characteristics of movement, supporting previous results. Reaching is initially performed between two points, but complex trajectories are also investigated through the inclusion of via- points. The addition of a gripper extends the model, allowing production of trajectories for grasping an object. Using the minimum variance principle to derive digit trajectories, a quantitative explanation for the approach of digits to the object surface is provided. These trajectories also exhibit human-like spatial and temporal coordination between hand transport and grip aperture. The model’s predictive ability is further tested in the perception of human demonstrated actions. Through integration with a system that performs perception using its motor system offline, in line with the motor theory of perception, the model is shown to correlate well with data on human perception of movement. These experiments investigate and extend the explanatory and predictive use of the model for human movement, and demonstrate that it can be suitably formulated to produce human-like movement on robot arms.Open acces

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

Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks

Autonomous robots need to interact with unknown, unstructured and changing environments, constantly facing novel challenges. Therefore, continuous online adaptation for lifelong-learning and the need of sample-efficient mechanisms to adapt to changes in the environment, the constraints, the tasks, or the robot itself are crucial. In this work, we propose a novel framework for probabilistic online motion planning with online adaptation based on a bio-inspired stochastic recurrent neural network. By using learning signals which mimic the intrinsic motivation signalcognitive dissonance in addition with a mental replay strategy to intensify experiences, the stochastic recurrent network can learn from few physical interactions and adapts to novel environments in seconds. We evaluate our online planning and adaptation framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is shown by learning unknown workspace constraints sample-efficiently from few physical interactions while following given way points.Comment: accepted in Neural Network