Action generation and action perception in imitation: an instance of the ideomotor principle.

Wohlschläger, A., Gattis, M., and Bekkering, H.
Philos Trans R Soc Lond B Biol Sci, 358:501–515, 2003
DOI, Google Scholar


We review a series of behavioural experiments on imitation in children and adults that test the predictions of a new theory of imitation. Most of the recent theories of imitation assume a direct visual-to-motor mapping between perceived and imitated movements. Based on our findings of systematic errors in imitation, the new theory of goal-directed imitation (GOADI) instead assumes that imitation is guided by cognitively specified goals. According to GOADI, the imitator does not imitate the observed movement as a whole, but rather decomposes it into its separate aspects. These aspects are hierarchically ordered, and the highest aspect becomes the imitator’s main goal. Other aspects become sub-goals. In accordance with the ideomotor principle, the main goal activates the motor programme that is most strongly associated with the achievement of that goal. When executed, this motor programme sometimes matches, and sometimes does not, the model’s movement. However, the main goal extracted from the model movement is almost always imitated correctly.


The authors report about a series of experiments which led them to propose a theory for imitation which gives the goal of a demonstrated movement a central role for imiation: GOADI – goal directed imitation. In particular they were looking at the errors made by children when imitating hand movements of a model. These movements were: touching your ear with your hand, touching spots on a table, pointing at or picking up an object. These experiments allowed to dissociate the goal of the corresponding movement from the way it is executed. In the ear touching movements, for example, the model touches her right ear with her left hand (a contralateral movement), but the child might imitate by touching its left ear with its left hand (an ipsilateral movement). In the whole paper they assume that people naturally imitate in a mirror fashion, i.e. you would touch your left ear when the model sitting opposite of you touches her right ear. This is backed up by the data in the sense that this is what people do in the vast majority of times.

Their theory is motivated by frequent CI errors of the children in which a contralateral movement is imitated with an ipsilateral movement, but the target of the movement is chosen correctly, i.e. the correct ear is touched. The authors conclude that the children determine the goal correctly, but don’t have enough working memory / attention to process all aspects of the demonstrated movements and simply execute the movement that achieves that goal and is most natural to them. In adults these kinds of errors are greatly reduced which is perhaps a result of greater attention abilities, but when the imitation task is made slightly more complicated similar errors can be observed.

Another important part of the theory suggests that demonstrated movements are decomposed into separate aspects and that these are ordered in a hierarchy (a goal and subgoals) such that aspects higher in the hierarchy are imitated with greater care. They report about experiments in which such a hierarchy seems to be observed for aspects object identitiy, object treatment, use of effector and movement (in this order). While there is a certain difference between object specific aspects and movement specific aspects, I’m not so certain about the strict hierarchy.

Anyway, the experiments are pretty convincing and strongly support a goal directed theory of imitation in contrast to theories which propose a direct mapping from sensory input to motor output.

Real-time Motion Retargeting to Highly Varied User-Created Morphologies.

Hecker, C., Raabe, B., Enslow, R. W., DeWeese, J., Maynard, J., and van Prooijen, K.
in: Proceedings of ACM SIGGRAPH ’08, 2008
URL, Google Scholar


Character animation in video games””whether manually key-framed or motion captured””has traditionally relied on codifying skeletons early in a game’s development, and creating animations rigidly tied to these fixed skeleton morphologies. This paper introduces a novel system for animating characters whose morphologies are unknown at the time the animation is created. Our authoring tool allows animators to describe motion using familiar posing and key-framing methods. The system records the data in a morphology-independent form, preserving both the animation’s structural relationships and its stylistic information. At runtime, the generalized data are applied to specific characters to yield pose goals that are supplied to a robust and efficient inverse kinematics solver. This system allows us to animate characters with highly varying skeleton morphologies that did not exist when the animation was authored, and, indeed, may be radically different than anything the original animator envisioned.


The paper explains how motion retargeting to wildly varying creatures is done in Electronic Arts’ game Spore. The crucial point is that they devised an animation system (Spasm) in which animators do not work on a fixed body, but on meta-level descriptions of body parts which are showcased on a small set of example bodies in the program window. Animators first select body parts by choosing descriptors like “grasper in front”. Then they can define movements in different modes e.g. relative to rest position, relative to external target, relative to limb length and similar others. The authors say in the discussion: “However, it takes weeks to build up an intuition about which kinds of motions generalize across a wide range of characters and which don’t.”

In principal they have devised an animation system in which motions are described in task space instead of in joint space (the standard in animation with e.g. Maya). Well, it’s some kind of hybrid. In general, everything in the paper is very ad hoc as the main objective is to make it work in real-time for the game. Anyway, the paper is not really addressing the problem of motion retargeting where you observe motion on one body and try to get it on another. Rather here they are concerned with representing motion from the beginning in such a way that it is easily transferred to a wide range of very different bodies.

A large part of the paper is about playing the so stored motion on a particular body (“specialization”). For this they use their own ad hoc IK solver. I didn’t find any interesting principles here (they sort out position of the spine first and only then solve for constraint satisfaction of the limbs), but I also didn’t put a lot of effort to understand what’s going on.