A method for quantifying clinical skill of at least one user, comprising: collecting motion data to be stored in at least one database, the motion data relating to at least one surgical task (T1;T2) done by at least one user using at least one surgical robot; segmenting the surgical motion data into plural surgemes (S1-S6) and plural dexemes (M1-M6), wherein each surgeme comprises a sub-task of the surgical task and each dexeme comprises a particular motion component of the sub-task, and wherein segmenting the motion data into surgemes and dexemes comprises normalising the motion data, projecting it to a lower dimension using linear discrimination analysis, and applying the following formula to the lower dimension data x t for every possible value of σ: P σ x b i,..., x e i = ∑ S b i ∈ S σ ∑ S b i + 1 ∈ S σ ⋯ ∑ S e i ∈ S σ ∏ t = b i p S t | S t − 1 N x t; μ s t, Σ S t where the value of σ that gives the maximum value of P is the segmentation that is used, b i and e i denote the begin and end time of σ i, S σ denotes the hidden states of the model for surgeme or dexeme σ, p(s|s') are the transition probabilities between these states, and N(•; μ s, ∑ s) is a multivariate Gaussian density with mean μ s and covariance ∑ s associated with state sεS σ; comparing the motion data for the at least one surgical task to other motion data for at least one other similar surgical task using at least one comparing module; quantifying the clinical skill of the at least one user, using the at least one comparing module, based on the comparing of the motion data for the at least one surgical task to the other motion data for the at least one other similar surgical task; determining, utilizing the at least one comparing module, at least one expert user based on the comparing; modeling, using at least one modeling module, at least one movement of the at least one expert user in terms of said surgemes and dexemes; teaching, using at least one teaching module, at least one novice user based on at least one modeled movement of the at least one expert user by guiding at least one movement of the novice user based on the modeled movement of the expert user. collecting motion data to be stored in at least one database, the motion data relating to at least one surgical task (T1;T2) done by at least one user using at least one surgical robot; segmenting the surgical motion data into plural surgemes (S1-S6) and plural dexemes (M1-M6), wherein each surgeme comprises a sub-task of the surgical task and each dexeme comprises a particular motion component of the sub-task, and wherein segmenting the motion data into surgemes and dexemes comprises normalising the motion data, projecting it to a lower dimension using linear discrimination analysis, and applying the following formula to the lower dimension data x t for every possible value of σ: P σ x b i,..., x e i = ∑ S b i ∈ S σ ∑ S b i + 1 ∈ S σ ⋯ ∑ S e i ∈ S σ ∏ t = b i p S t | S t − 1 N x t; μ s t, Σ S t where the value of σ that gives the maximum value of P is the segmentation that is used, b i and e i denote the begin and end time of σ i, S σ denotes the hidden states of the model for surgeme or dexeme σ, p(s|s') are the transition probabilities between these states, and N(•; μ s, ∑ s) is a multivariate Gaussian density with mean μ s and covariance ∑ s associated with state sεS σ; comparing the motion data for the at least one surgical task to other motion data for at least one other similar surgical task using at least one comparing module; quantifying the clinical skill of the at least one user, using the at least one comparing module, based on the comparing of the motion data for the at least one surgical task to the other motion data for the at least one other similar surgical task; determining, utilizing the at least one comparing module, at least one expert user based on the comparing; modeling, using at least one modeling module, at least one movement of the at least one expert user in terms of said surgemes and dexemes; teaching, using at least one teaching module, at least one novice user based on at least one modeled movement of the at least one expert user by guiding at least one movement of the novice user based on the modeled movement of the expert user.