生物谷：據英國廣播公司報導，在英國斯特靈大學攻讀博士學位的華人學者耿濤最近研製成功能像真人一樣雙腿直立行走的機器人――Runbot，而且和人類的最快行走速度差不多。
這一成果發表在了最新一期的《計算生物學公共圖書館期刊》上。
耿濤是在一名英國導師和一位德國教授的指導下完成這一研究的。這一成果的突出意義在於，這個直立雙腿機器人的行走原理和真人一樣，而且具有適應地面狀況的學習能力。
耿濤的研究借用了20世紀30年代蘇聯運動生理學家尼古拉·伯恩斯坦的「運動感覺修正」原則，道理相當於控制論的「反饋」概念。
機器人的直立行走一直是科學家們久攻不下的難題，也是實現機器人「仿真」的關鍵一步。
近年這方面的研究興趣集中在了對人類行走模式的研究，引入了很多生物學原理。
傳統的觀點認為，要像人類一樣行走，機器人必須有一個隨時控制平衡和步伐節奏的「中樞」。
號稱世界最先進步行機器人的日本本田公司Asimo就是這類「動態平衡」機器人，需要隨時計算每一步的平衡，而且走路像是京劇中的「方步」。
但根據伯恩斯坦曾一度被人遺忘的觀點，腿部的「局部反射」而非大腦的「中央控制」在人類的行走中起更大作用。
如果仔細觀察，人類的行走實際是一連串不斷向前「跌倒」又不斷支撐自己的過程。
根據這一原理，義大利科學家曾經製成了一部叫做「呵啷呵啷」機械腿，能夠沿著坡道靠重力「走」下來。
這次耿濤推出的Runbot則不僅可以和人一樣落前腳、抬後腳，邁步行走，而且步速可以達到每秒3.5個腿長，和人類的最快行走速度差不多，也比現有的雙腿機器人步速快一倍以上。
更引人注目的是，30釐米高的Runbot除了靠腿部的關節、傳感器被動行走之外，還可以通過「大腦」主動學習，判斷並適應地形。
美中不足的是，Runbot還不能實現自主左右平衡，需要牽引來避免側向傾倒。
但耿濤的成果不僅使仿真機器人的研究向前邁出了重大一步，也將對脊髓損傷癱瘓病人的治療提供借鑑。（中新網）
原始出處:
Received: January 26, 2007; Accepted: May 30, 2007; Published: July 13, 2007
Poramate Manoonpong1, Tao Geng2, Tomas Kulvicius1, Bernd Porr3, Florentin Wörgötter1,2*
1 Bernstein Center for Computational Neuroscience, University of Göttingen, Göttingen, Germany, 2 Department of Psychology, University of Stirling, Stirling, Scotland, United Kingdom, 3 Department of Electronics and Electrical Engineering, University of Glasgow, Glasgow, Scotland, United Kingdom
Human walking is a dynamic, partly self-stabilizing process relying on the interaction of the biomechanical design with its neuronal control. The coordination of this process is a very difficult problem, and it has been suggested that it involves a hierarchy of levels, where the lower ones, e.g., interactions between muscles and the spinal cord, are largely autonomous, and where higher level control (e.g., cortical) arises only pointwise, as needed. This requires an architecture of several nested, sensori–motor loops where the walking process provides feedback signals to the walker's sensory systems, which can be used to coordinate its movements. To complicate the situation, at a maximal walking speed of more than four leg-lengths per second, the cycle period available to coordinate all these loops is rather short. In this study we present a planar biped robot, which uses the design principle of nested loops to combine the self-stabilizing properties of its biomechanical design with several levels of neuronal control. Specifically, we show how to adapt control by including online learning mechanisms based on simulated synaptic plasticity. This robot can walk with a high speed (>3.0 leg length/s), self-adapting to minor disturbances, and reacting in a robust way to abruptly induced gait changes. At the same time, it can learn walking on different terrains, requiring only few learning experiences. This study shows that the tight coupling of physical with neuronal control, guided by sensory feedback from the walking pattern itself, combined with synaptic learning may be a way forward to better understand and solve coordination problems in other complex motor tasks.
Figure 1.Relative Leg-Length and Maximum Relative Speed of Various Planar Biped Robots
(A) A copy of McGeer's planar passive biped robot walking down a slope [77].
(B) 「Mike,」 similar to McGeer's robot, but equipped with pneumatic actuators at its hip joints. Thus it can walk half passively on level ground [77].
(C) 「Spring Flamingo,」 a powered planar biped robot with actuated ankle joints [78].
(D) Rabbit, a powered biped with four degrees of freedom and pointed feet [79].
(E) RunBot.
(F) The world record for the fastest human's walking speed [80,81].
全文連結：
http://compbiol.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pcbi.0030134
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