Imitation Learning: A New Approach in Artificial Life Animation

Transcription

1 38 4 Vol. 38, No ACTA AUTOMATICA SINICA April, 2012 : ,,.,.,,,. PhysX,,. DOI,,,,,,. :., 2012, 38(4): /SP.J Imitation Learning: A New Approach in Artificial Life Animation BAN Xiao-Juan 1 XU Zhuo-Ran 1 LIU Hao 1 Abstract This paper proposes a new artificial life animation approach imitation learning. Imitation is a highly effective learning method for acquiring motion skill which can be regarded as a set of numerous motion sequences. Imitating representative motion sequences to acquire the local motion skill and generalizing them can achieve the entire motion skill. The cores of imitation learning are motion similarity and simple-compose behavior methodology, and evolutionary computation is used as an optimization method. Imitation learning decreases the dependence of evolutionary computation on traditional fitness function and the time spent on designing a suitable fitness function. It also increases the ability of optimizing complex goal. So it increases the efficiency of producing animation. We verify our method by training an artificial cat robot to learn landing behavior based on PhysX simulation framework, which achieves a good result. Key words Imitation learning, simple-compose behavior, motion similarity matching, evolutionary computation, artificial life animation Citation Ban Xiao-Juan, Xu Zhuo-Ran, Liu Hao. Imitation learning: a new approach in artificial life animation. Acta Automatica Sinica, 2012, 38(4): ,.,,,., :.,. Tu [1] Manuscript received July 18, 2011; accepted November 10, 2011 ( ), ( ), (FRF-TP B), (NCET ) Supported by National Natural Science Foundation of China ( ), Beijing Natural Science Foundation ( ), Fundamental Research Funds for the Central Universities (FRF-TP B), and Program for New Century Excellent Talents in University of Ministry of Education of China (NCET ) Recommended by Associate Editor SUN Chang-Yin School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing ,.. Wu [2],.,. Liu [3],...,,.. Sims [4 5]. Sims,,.,. Sims,. Tanev [6]

2 4 : : 519,,..,,.,.,. Secretan [7].,.,,,,. PhysX,,.,. M 1, M 2,, M n F. 2, 1..,..,.,.. 1,. Marriott [8].,. :,,.., F f. 1. F : O = F (I). I, O. I X, O Y, X, Y. 2. F M, f : O = f(i ). I x, O y, x X, y Y M. 1. O = F (I ), O = f(i). f x F, i X, i x F. f 1, f 2,, f n, F : O = f k (I), k = T (I) (1) T, f 1, f 2,, f n f k F, T f k. 3., F. M 1, M 2,, M n, M n F. : 1) M 1, M 2,, M n f 1, f 2,, f n ; 2), T F. f T, Fig System structure of imitation learning, Pullen [9]. Liu [10],. Lee [11]..,.,, ,., root, 2 1 root ; 2 3 root ; ,. M M = (F 1, F 2,, F T M ). : F = (r 2,, r 15 ), r n R 3 n.

3 j b j = (front(i, j), left(i, j), above(i, j)) (2), i j. front, left, above j i, r n. B = (b 2,, b 15 ) (3) (Principle component analysis, PCA) [12] B L, Fig. 2 SF = (L 1, L 2,, L T M ) (4) 2 Model of joints and bones of an artificial cat 3.2,. T F = (P 2, P 3,, P 15 ) (5), P i i b i. p i (0, 0, 0 0, 0, 0) p i (0, 0, 0 1, 1, 1) P i =..... p i (1, 1, 1 0, 0, 0) p i (1, 1, 1 1, 1, 1) (6), p i (n, m, p k, w, z) b i = (n, m, p), b i = (k, w, z). 4, [13 15] R. Kahveci [16] CS-index. Lee [17],.,. 4.1,, (Dynamic time warping, DTW),. SF SF. D = 1 2 (DT W (SF, SF ) + DT W (SF, SF )) (7), DT W (SF, SF ) SF DTW, DT W (SF, SF ) SF DTW. DTW : 1) n n DT W [n, n], n = T M. DT W [0, 0] = 0, DT W [1,, n, 1,, n] =. 2) for i = 1 to n for j = 1 to n a) d = L i L i b) DT W [i, j] = d + min(dt W [i 1, j], DT W [i, j 1], DT W [i 1, j 1]) 3) DT W [n, n] DTW. 4.2 : S = 15 i=2 P i P i (8), P, P. 5 :.... : 1),.. 2) (1).,,.,. : 1), ; 2) ; 3), ; 4) 2),. Gomez [18]. Stanley [19].

4 4 : : 521 Dürr [20] (Analog genetic encoding).,.,..,.,,.,,,. : G unit = I, O, h, W. :.....,.,. 6,,.,,, 3. NVIDIA PhysX. 6.1,, x-y x-z, 60. x-y y-z, ± Fig. 5 5 Side view and bottom view of artificial cat model y S y1, S y2 ;, S s1, S s2, S t1, S t2 ; S h,, S l1, S l2,, S l ,.,,.,,, S s1 = S s2 = 0. 6 Fig. 6 Artificial cat controller Fig. 3 Landing behavior of a cat 4 Fig. 4 Front view of artificial cat model S s1, S s2, S t1, S t2, S y1, S y2, S h, S l1, S l2,, S l8 I T, 1.

5 Table 1 Gene unit values ANN Spine I T V s1, V s2 Tail I T, V s1, V s2 V t1, V t2 Legs I T, V s1, V s2 V l1, V l2,, V l8 Compose I T s 1, s 2, s 3 1, V s1, V s2, V t1, V t2, S s1, S s2, S t1, S t2. V l1, V l2,, V l8, S l1, S l2,, S l8. s 1, s 2, s 3.,.,,.,. 6.4 : 3,. 4 S y1, S y2.,. 1/100 (s), 200., f = Average(H) V ariance(h) + 1 (9), H = {S h (t) t > T }. S h (t) S h t. T S h (t) < 500 mm. : f, D, S. : f 1 < f 2, f 1 f 2 M 1 < M 2 D 1 < D 2, f 1 = f 2, D 1 D 2 S 1 < S 2, f 1 = f 2, D 1 = D 2 (10) : N 1 < N 2 bigger(n 1, N 2 ) < bigger(n 2, N 1 ) (11), bigger(n 1, N 2 ) N 1 N ,,.,, V s1 = f 1 (t), V s2 = f 2 (t).,,. 2, ,. 1, f 1. 2, 1. 7.,. 7, 1 f 1,.,,. 2, D 2 S 2, f 2., Fig. 7 Fitness and motion similarity in Trial 1 and Trial ,. 3, , S y1, S y2. 90, 90., S h. 8 9,,,,.,.

6 4 : : Fig. 8 Angle between body and ground in Trial Fig. 11 Height of joint on spine in Trial Fig. 9 Height of joint on spine in Trial ( 8),. 11 ( 9),,,, Fig. 12 Angle between body and ground in Trial Fig. 13 Height of joint on spine in Trial Fig. 10 Angle between body and ground in Trial ,,. 12 ( 8),. 13 ( 9),,., ,,,.,.,,..

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