Abstract
This paper presents a new learning control framework for digital human models in a physics-based virtual environment. The novelty of our controller is that it combines multi-objective control based on human properties (combined feedforward and feedback controller) with a learning technique based on human learning properties (human-being’s ability to learn novel task dynamics through the minimization of instability, error and effort). This controller performs multiple tasks simultaneously (balance, non-sliding contacts, manipulation) in real time and adapts feedforward force as well as impedance to counter environmental disturbances. It is very useful to deal with unstable manipulations, such as tool-use tasks, and to compensate for perturbations. An interesting property of our controller is that it is implemented in Cartesian space with joint stiffness, damping and torque learning in a multi-objective control framework. The relevance of the proposed control method to model human motor adaptation has been demonstrated by various simulations.
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Appendices
Appendix A: Relation between Cartesian space and joint space
Using Eqs. 1, the interaction dynamics is:
Given an interaction wrench \(W^{i}_\mathrm{end}\).
In this paper, we treat the DHM control where the floating base is the foot. We consider cases with the foot fixed to the ground. In this way, we obtain a completely actuated DHM with fixed-base robots characteristics. The dynamic model of DHM is:
with \(M_{q} = L^\mathrm{T} M L\), \(N_{q} = L^\mathrm{T} N L\), \(G_{q} = L^\mathrm{T} G\), \(J_{c,q}^\mathrm{T} = L^\mathrm{T} J_{c}^\mathrm{T}\) and \(J_{\mathrm{end},q}^\mathrm{T} = L^\mathrm{T} J_\mathrm{end}^\mathrm{T}\). When the only contact with the ground is the foot and it is the root, we obtain \(J_{c} L = 0\).
Since \(\rho = Sq\) and \(S\) is a matrix to select a part of the actuated degrees of freedom (\(S=[I\,0]\)) to obtain a dynamic model independent of non-sliding contact forces at known fixed locations in Eq. 1 such as the contacts between the feet and the ground, we can write the system as:
with \(M_{\rho } = S M_{q} S^\mathrm{T}\), \(N_{\rho } = S N_{q} S^\mathrm{T}\), \(G_{\rho } = S G_{q}\) and \(J_{\mathrm{end},\rho }^\mathrm{T} = S J_{\mathrm{end},q}^\mathrm{T}\).
From Eq. 35 and since \(\delta W^{i}_\mathrm{end} = K_\mathrm{end} \hbox {vec}(H^{-1}_\mathrm{end}\delta H_\mathrm{end}) = K_\mathrm{end} J_{\mathrm{end},q} \delta q = K_\mathrm{end} J_{\mathrm{end},q} \delta (S^t \rho ) = K_\mathrm{end} J_{\mathrm{end},\rho } \delta \rho \), we obtain:
Since \(\delta \tau _{\rho } = - K_{\rho } \delta \rho \) and Eq. 36, we obtain:
Finally, the Cartesian impedance is:
with \(J^{\dag }_{\mathrm{end},\rho }\) the dynamic pseudo-inverse [38] defined as:
It can be similarly obtained \(B_\mathrm{end} = J_\mathrm{end,\rho }^{\dag T} B_{\rho } J_{\mathrm{end},\rho }^{\dag }\).
Appendix B: Convergence analysis
1.1 B.1 Motion error cost function
The first derivative of \(M_\mathrm{E}\) (Eq. 5) can be calculated as follows:
with
and \(V^{*}=V^{d} - b\delta (H^{d},H^\mathrm{r})\). \(V^{d}\) is the velocity obtained by minimum jerk planner. \(A^{*}\) is the derivative of \(V^{*}\).
Matrix \(M_{\rho }\) is symmetric, we therefore obtain:
The relationship between \(\rho \) velocity and Cartesian space velocity can be expressed as:
Differentiating Eq. 43, the Cartesian acceleration term can be found as:
then the equation of robot motion in joint space can also be represented in Cartesian space coordinates by the relationship:
Substituting Eqs. 45 and 43 into Eq. 35 yields:
Multiplying both side by \(J_{\mathrm{end},\rho }^{\dag T}\), we obtain:
Using Eq. 10, we obtain:
where \(\tau _{\rho }^\mathrm{f\!f}\) is the torque to compensate for DHM dynamics. By definition, it can be written as:
Using Eq. 41 and substituting Eq. 49 into Eq. 48 yields:
Substituting Eq. 50 into Eq. 42 yields:
Matrix \(\dot{M_{\rho }} - 2N\) is skew-symmetry [60] and for this reason, we have:
Let us now analyze the third term of Eq. 51. Using Eq. 39, since \(J_{\mathrm{end},\rho }J^{\dag }_{\mathrm{end}, \rho }=I\) and \(\dot{J}_{\mathrm{end},\rho }J_{\mathrm{end},\rho }^{\dag }+J_{\mathrm{end},\rho }\dot{J}^{\dag }_{\mathrm{end},\rho }=0\), we obtain:
Substituting Eqs. 52 and 53 into Eq. 51, we obtain:
Using Eqs. 54, 11 and 38, we have:
We can derive \(\delta M_E(t) = M_E(t)-M_E(t-D)\) from Eqs. 55 and 8 as:
Any smooth interaction force can be approximated by the linear terms of its Taylor expansion along the reference trajectory as follows:
where \(W^{i,0}_\mathrm{end}\) is the zero order term compensated by \(J_{\mathrm{end},\rho }^{\dag T} \tau ^\mathrm{min}\); \([J_\mathrm{end}^{\dag T} K^{i}_{\rho } J_\mathrm{end}^{\dag }]\) and \([J_\mathrm{end}^{\dag T} B^{i}_{\rho } J_\mathrm{end}^{\dag }]\) are the first-order coefficients. From Eqs. 57 and 41, we can obtain the values for \(K^\mathrm{min}_{\rho }(t)\), \(B^\mathrm{min}_{\rho }(t)\) and \(\tau ^\mathrm{min}_{\rho }(t)\) to guarantee stability (Eq. 58). Different \(W^{i}_\mathrm{end}\) will yield different values of \(K^\mathrm{min}_{\rho }(t)\), \(B^\mathrm{min}_{\rho }(t)\) and \(\tau ^\mathrm{min}_{\rho }(t)\) and when \(W^{i}_\mathrm{end}\) is zero or is assisting the tracking task \(||\epsilon (t)|| \rightarrow 0\), \(K^\mathrm{min}_{\rho }(t)\), \(B^\mathrm{min}_{\rho }(t)\) and \(\tau ^\mathrm{min}_{\rho }(t)\) will be \(0\).
\(K^\mathrm{min}_{\rho }(t)\), \(D^\mathrm{min}_{\rho }(t)\) and \(\tau ^\mathrm{min}_{\rho }(t)\) represent the minimal required effort of stiffness, damping and feedforward force required to guarantee
so that from Eq. 55 we have \(\int _{t-D}^{t} \dot{M_\mathrm{E}}(\sigma )\, \hbox {d} \sigma \le 0\).
From Eqs. 56 and 58, we can write:
1.2 B.2 Metabolic cost function
The metabolic cost function is:
According to the definition of \(\Phi (t)\) and \(Q\), the following properties of \(\hbox {vec}(\cdot )\), \(\otimes \) and \(\hbox {tr}(\cdot )\) operators:
and using the symmetry of \(Q_K^{-1}\), we obtain:
In the same way, can be found the terms corresponding to \(\tilde{B}\) and \(\tilde{\tau }\).
For these reasons, we can define \(\delta M_\mathrm{C}(t)= M_\mathrm{C}(t)-M_\mathrm{C}(t-D)\) as:
From Eqs. 13, 14 and 16, we obtain:
Using the symmetry of \(Q_K^{-1}\), \(\tilde{K}(\sigma )-\tilde{K}(\sigma -D)=\delta K(\sigma )\) and Eq. 64, the first term in the integrand of Eq. 63 can be written as:
In the same way, can be found the second terms in the integrand of Eq. 63 as:
and third terms in the integrand of Eq. 63 as:
Replacing Eqs. 65, 66 and 67 into 63, we obtain:
Combining Eqs. 59 and 68, we obtain the first difference of cost function:
To obtain \(\delta C(t) \le 0\), a sufficient condition is:
where \(\lambda _{B}\) as the infimum of the smallest eigenvalue of \(B^\mathrm{ini}_\mathrm{end}\).
Replacing \(\gamma (t) = \frac{p}{1 + u ||{\epsilon (t)}||^2}\) into Eq. 70, we obtain:
To find the regions of points \((||\epsilon ||^2,||\tilde{\Phi }||)\) for each of which Eq. 71 holds, we need firstly to determine the set of points that satisfies:
Equation 72 is an ellipse passing through the points \((||\epsilon ||^2 = 0,||\tilde{\Phi }||=0)\) and \((||\epsilon ||^2 = 0,||\tilde{\Phi }||=||{\Phi }^d||)\).
To find the canonical equation of this ellipse, we need only to complete the squares and we obtain:
By Krasovskii–LaSalle principle, \(||\epsilon ||^2\) and \(||\tilde{\Phi }||\) will converge to an invariant set \(\Omega _s \subseteq \Omega \) on which \(\delta C(t)=0\), where \(\Omega \) is the bounding set defined as:
If the parameter \(\gamma \) is constant [24], the bounding set is:
\(\gamma \) does not affect convergence, but the convergence speed and size of convergence set.
Appendix C: Minjerk
1.1 C.1 Formal definition
Using Eq. 20, we can write the inside term of the integral as:
To explicit the invariance with respect to rotations and translations of the minimization problem in Eq. 76, we can define uniquely 3D curve [56] by its curvature \(R(s)\) and its torsion \(\eta (s)\).
The path \(\mathbf r \) satisfies Frenet’s formulas:
From geometry, we know that:
We replacing Eq. 78 in Eq. 76 and we obtain:
\(\mathbf n \), \(\mathbf t \) and \(\mathbf b \) are orthogonal and thus we obtain:
1.2 C.2 Relation to the 2/3 power law
We want to find the relation of Eq. 80 to 2/3 power law.
To obtain this, we define a function:
\(Z_s\) corresponds to the term multiplying the torsion \(\eta \) in Eq. 80.
We derive Eq. 81 respect to time and we obtain:
The term \(R'(s)\dot{s}^3+3\dot{s}\ddot{s}R(s)\) is equal to the term multiplying \(\mathbf n \) in Eq. 79. We now substitute Eq. 82 in Eq. 79:
From Eq. 81, we have:
In the \(2/3\) power law \(Z_s^{\frac{1}{3}}=\hbox {const}\) and \(Z_s'=0\) and it is equivalent to setting the coefficient of n of the instantaneous jerk to zero, and the coefficient of b proportional to the coefficient of t. To demonstrate this, we analyze the 2D power law:
Taking derivatives, we obtain:
The jerk vector points are orthogonal to n and aligned with t. Thus, the jerk along n is zero.
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Magistris, G.D., Micaelli, A., Evrard, P. et al. A human-like learning control for digital human models in a physics-based virtual environment. Vis Comput 31, 423–440 (2015). https://doi.org/10.1007/s00371-014-0939-0
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DOI: https://doi.org/10.1007/s00371-014-0939-0