Robots that can adapt like animals

As robots leave the controlled environments of factories to autonomously function in more complex, natural environments, they will have to respond to the inevitable fact that they will become damaged. However, while animals can quickly adapt to a wide variety of injuries, current robots cannot "think outside the box" to find a compensatory behavior when damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots. Here we introduce an intelligent trial and error algorithm that allows robots to adapt to damage in less than two minutes, without requiring self-diagnosis or pre-specified contingency plans. Before deployment, a robot exploits a novel algorithm to create a detailed map of the space of high-performing behaviors: This map represents the robot's intuitions about what behaviors it can perform and their value. If the robot is damaged, it uses these intuitions to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a compensatory behavior that works in spite of the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new technique will enable more robust, effective, autonomous robots, and suggests principles that animals may use to adapt to injury

Locomotion Control of Hexapod Walking Robot with Four Degrees of Freedom per Leg

V této práci představujeme nového šestinohého robota jménem HAntR, kterého jsme vytvořili dle potřeb Laboratoře výpočetní robotiky Centra umělé inteligence fakulty Elektrotechnické Českého vysokého učení technického v Praze. Jeho hlavním účelem jest vylepšit schopnosti pohybu v těžkém terénu původního robotu přidáním čtvrtého stupně volnosti každé noze. Na základě nově navržené nohy jsme také přepracovali celé tělo robotu tak, aby splnilo i další požadavky, jako například menší rozměry, či možnost osazení alespoň šesti Lithium-Iontovými monočlánky. V práci pečlivě popisujeme motivace a úvahy, které nás k výslednému návrhu vedly. Uvádíme řešení přímé i inverzní kinematické úlohy řešené pomocí podmínky na ideální orientaci konce nohy a uvažující i důležité kinematické singularity. Navržený robot byl vyzkoušen v několika experimentech, při kterých byl použit námi navržený řídicí systém napsaný v jazyce C++. Ukázalo se, že HAntR vydrží díky zvýšené energetické hustotě a lepšímu rozkladu sil v končetinách autonomně fungovat přes hodinu. Robot je také schopen jít rychlostí až 0.42m/s, což předčí mnohé srovnatelné roboty. Při experimentu, kdy robot stál na nakloněné rovině, bylo prokázáno zlepšení oproti předchozímu robotu. A také jsme dle pokynů této práce potvrdili, že i HAntR je schopen adaptivní chůze spoléhající pouze na poziční zpětnou vazbu.In this thesis a novel six-legged robot called HAntR is presented. The robot was developed according to needs of the Robotics Laboratory, at the Artificial Intelligent Center, Faculty of Electrical Engineering, Czech Technical University in Prague. Its main purpose is enhancing rough-terrain movement capabilities by upgrading a former design by adding fourth degree of freedom to each leg. We also revised robot torso to fit new leg design and incorporate other requirements such as smaller dimensions with space for at least six Lithium-Ion cells. We thoroughly describe motivations and considerations that led us to the presented particular solution. Further, the solutions of forward and inverse kinematic tasks with partial orientation constraint and important singularities avoidance are presented. The proposed design has been evaluated in several experimental deployments, which utilised developed software controller written in C++. Endurance tests showed, that HAntR is able to remotely operate for over an hour thanks to increased energy density. Maximal speed test resulted to 0.42m/s during tripod gait, which outpaces most of the comparable robotic platforms. Experiment where HAntR stood on platform with varying inclination showed qualitative improvement against former robot. Finally, in accord with the thesis assignment, we proved that HAntR is able to perform walking with adaptive gait using positional feedback only

A methodology for the Lower Limb Robotic Rehabilitation system

The overall goal of this thesis is to develop a new functional lower limb robot-assisted rehabilitation system for people with a paretic lower limb. A unilateral rehabilitation method is investigated, where the robot acts as an assistive device to provide the impaired leg therapeutic training through simulating the kinematics and dynamics of the ankle and lower leg movements. Foot trajectories of healthy subjects and post-stroke patients were recorded by a dedicated optical motion tracking system in a clinical gait measurement laboratory. A prototype 6 degrees of freedom parallel robot was initially built in order to verify capability of achieving singularity-free foot trajectories of healthy subjects in various exercises. This was then followed by building and testing another larger parallel robot to investigate the real-sized foot trajectories of patients. The overall results verify the designed robot’s capability in successfully tracking foot trajectories during different exercises. The thesis finally proposes a system of bilateral rehabilitation based on the concept of self-learning, where a passive parallel mechanism follows and records motion signatures of the patient’s healthy leg, and an active parallel mechanism provides motion for the impaired leg based on the kinematic mapping of the motion produced by the passive mechanism

Information transfer and causality in the sensorimotor loop

This thesis investigates information-theoretic tools for detecting and describing causal influences in embodied agents. It presents an analysis of philosophical and statistical approaches to causation, and in particular focuses on causal Bayes nets and transfer entropy. It argues for a novel perspective that explicitly incorporates the epistemological role of information as a tool for inference. This approach clarifies and resolves some of the known problems associated with such methods. Here it is argued, through a series of experiments, mathematical results and some philosophical accounts, that universally applicable measures of causal influence strength are unlikely to exist. Instead, the focus should be on the role that information-theoretic tools can play in inferential tests for causal relationships in embodied agents particularly, and dynamical systems in general. This thesis details how these two approaches differ. Following directly from these arguments, the thesis proposes a concept of “hidden” information transfer to describe situations where causal influences passing through a chain of variables may be more easily detected at the end-points than at intermediate nodes. This is described using theoretical examples, and also appears in the information dynamics of computer-simulated and real robots developed herein. Practical examples include some minimal models of agent-environment systems, but also a novel complete system for generating locomotion gait patterns using a biologically-inspired decentralized architecture on a walking robotic hexapod