def __init__(self, model, endeff_frame_names):
def getFrameId(name):
idx = self.robot.model.getFrameId(name)
if idx == len(self.robot.model.frames):
raise Exception('Unknown frame name: {}'.format(name))
return idx
self.robot = RobotWrapper(model)
self.robot_mass = sum([i.mass for i in self.robot.model.inertias[1:]])
self.base_id = getFrameId('base_link')
self.endeff_frame_names = endeff_frame_names
self.endeff_ids = [getFrameId(name) for name in endeff_frame_names]
self.is_init_time = True
self.ne = len(self.endeff_ids)
self.nv = self.robot.model.nv
# Tracking weights
self.w_endeff_tracking = 10**5
self.w_endeff_contact = 10**5
self.w_lin_mom_tracking = 100.0
self.w_ang_mom_tracking = 10.0
self.w_joint_regularization = 0.1
# P and D gains for tracking the position
self.p_endeff_tracking = 10000.
self.d_endeff_tracking = 200
self.p_com_tracking = 100.
self.p_orient_tracking = 10.
self.d_orient_tracking = 1.
self.p_mom_tracking = 10. * np.array([1., 1., 1., .01, .01, .01])
self.p_joint_regularization = 1.
self.d_joint_regularization = .5
self.desired_acceleration = np.zeros(
((self.ne + 2) * 3 + (self.nv - 6), ))
# Allocate space for the jacobian and desired velocities.
# Using two entires for the linear and angular velocity of the base.
# (self.nv - 6) is the number of joints for posture regularization
self.J = np.zeros(((self.ne + 2) * 3 + (self.nv - 6), self.nv))
self.drift_terms = np.zeros_like(
self.desired_acceleration) #i.e. dJ * dq
self.measured_velocities = np.zeros((self.J.shape[0], )) # i.e. J * dq
self.use_hierarchy = False
self.qp_solver = QpSolver()
def __init__(self, model, endeff_frame_names):
def getFrameId(name):
idx = model.getFrameId(name)
if idx == len(model.frames):
raise Exception('Unknown frame name: {}'.format(name))
return idx
self.robot = RobotWrapper(model)
self.model = model
self.data = self.robot.data
self.mass = sum([i.mass for i in self.robot.model.inertias[1:]])
self.base_id = self.robot.model.getFrameId('base_link')
self.endeff_frame_names = endeff_frame_names
self.endeff_ids = [getFrameId(name) for name in endeff_frame_names]
self.is_init_time = 1
self.ne = len(self.endeff_ids)
self.nv = self.model.nv
# Tracking weights
self.w_endeff_tracking = 1.
self.w_endeff_contact = 1.
self.w_lin_mom_tracking = 1.0
self.w_ang_mom_tracking = 1.0
self.w_joint_regularization = 0.01
# P gains for tracking the position
self.p_endeff_tracking = 1.
self.p_com_tracking = 1.
self.last_q = None
self.last_dq = None
# Allocate space for the jacobian and desired velocities.
# Using two entires for the linear and angular velocity of the base.
# (self.nv - 6) is the number of jointss for posture regularization
self.J = np.zeros(((self.ne + 2) * 3 + (self.nv - 6), self.nv))
self.vel_des = zero((self.ne + 2) * 3 + (self.nv - 6))
self.mom_ref_air_phase = zero(6)
self.qp_solver = QpSolver()
class PointContactInverseKinematics(object):
def __init__(self, model, endeff_frame_names):
def getFrameId(name):
idx = model.getFrameId(name)
if idx == len(model.frames):
raise Exception('Unknown frame name: {}'.format(name))
return idx
self.robot = RobotWrapper(model)
self.model = model
self.data = self.robot.data
self.mass = sum([i.mass for i in self.robot.model.inertias[1:]])
self.base_id = self.robot.model.getFrameId('base_link')
self.endeff_frame_names = endeff_frame_names
self.endeff_ids = [getFrameId(name) for name in endeff_frame_names]
self.is_init_time = 1
self.ne = len(self.endeff_ids)
self.nv = self.model.nv
# Tracking weights
self.w_endeff_tracking = 1.
self.w_endeff_contact = 1.
self.w_lin_mom_tracking = 1.0
self.w_ang_mom_tracking = 1.0
self.w_joint_regularization = 0.01
# P gains for tracking the position
self.p_endeff_tracking = 1.
self.p_com_tracking = 1.
self.last_q = None
self.last_dq = None
# Allocate space for the jacobian and desired velocities.
# Using two entires for the linear and angular velocity of the base.
# (self.nv - 6) is the number of jointss for posture regularization
self.J = np.zeros(((self.ne + 2) * 3 + (self.nv - 6), self.nv))
self.vel_des = zero((self.ne + 2) * 3 + (self.nv - 6))
self.qp_solver = QpSolver()
def rotate_J(self, jac, index):
world_R_joint = se3.SE3(self.data.oMf[index].rotation, zero(3))
return world_R_joint.action.dot(jac)
def get_world_oriented_frame_jacobian(self, q, index):
return self.rotate_J(
se3.getFrameJacobian(self.model, self.data, index,
se3.ReferenceFrame.LOCAL), index)
def fill_jacobians(self, q):
# REVIEW(jviereck): The Ludo invkin sets the angular momentum part to the identity.
self.J[:6, :] = self.robot.data.Ag
# self.J[:3, :] = robot.data.Jcom * invkin.mass
for i, idx in enumerate(self.endeff_ids):
self.J[6 + 3 * i:6 + 3 *
(i + 1), :] = self.get_world_oriented_frame_jacobian(
q, idx)[:3]
# this is joint regularization part
self.J[(self.ne + 2) * 3:, 6:] = np.identity(self.nv - 6)
#print "jac:\n",self.J,"\n\n"
def fill_vel_des(self, q, dq, com_ref, lmom_ref, amom_ref, endeff_pos_ref,
endeff_vel_ref, joint_regularization_ref):
self.vel_des[:3] = (lmom_ref + self.p_com_tracking *
(com_ref - self.robot.com(q).T)).T
self.vel_des[3:6] = amom_ref
for i, idx in enumerate(self.endeff_ids):
if self.is_init_time:
self.vel_des[6 + 3*i: 6 + 3*(i + 1)] = endeff_vel_ref[i] + \
1. * (endeff_pos_ref[i] - self.robot.data.oMf[idx].translation.T).T
else:
self.vel_des[6 + 3*i: 6 + 3*(i + 1)] = endeff_vel_ref[i] + \
self.p_endeff_tracking * (
endeff_pos_ref[i] - self.robot.data.oMf[idx].translation.T).T
if joint_regularization_ref is None:
self.vel_des[(self.ne + 2) * 3:] = zero(self.nv - 6)
else:
self.vel_des[(self.ne + 2) * 3:] = joint_regularization_ref
# print "vel:\n",self.vel_des,"\n\n"
def fill_weights(self, endeff_contact):
w = [
self.w_lin_mom_tracking * np.ones(3),
self.w_ang_mom_tracking * np.ones(3)
]
for eff in endeff_contact:
if eff == 1.: # If in contact
w.append(self.w_endeff_contact * np.ones(3))
else:
w.append(self.w_endeff_tracking * np.ones(3))
w.append(self.w_joint_regularization * np.ones(self.nv - 6))
self.w = np.diag(np.hstack(w))
# print("w:",w,"\n\n")
def forward_robot(self, q, dq):
# Update the pinocchio model.
self.robot.forwardKinematics(q, dq)
self.robot.computeJointJacobians(q)
self.robot.framesForwardKinematics(q)
se3.ccrba(self.robot.model, self.robot.data, q, dq)
self.last_q = q.copy()
self.last_dq = dq.copy()
def compute(self, q, dq, com_ref, lmom_ref, amom_ref, endeff_pos_ref,
endeff_vel_ref, endeff_contact, joint_regularization_ref):
'''
Arguments:
q: Current robot state
dq: Current robot velocity
com_ref: Reference com position in global coordinates
lmom_ref: Reference linear momentum in global coordinates
amom_ref: Reference angular momentum in global coordinates
endeff_pos_ref: [N_endeff x 3] Reference endeffectors position in global coordinates
endeff_vel_ref: [N_endeff x 3] Reference endeffectors velocity in global coordinates
'''
if not np.all(np.equal(self.last_q, q)) or np.all(
np.equal(self.last_dq, dq)):
self.forward_robot(q, dq)
self.fill_jacobians(q)
self.fill_vel_des(q, dq, com_ref, lmom_ref, amom_ref, endeff_pos_ref,
endeff_vel_ref, joint_regularization_ref)
self.fill_weights(endeff_contact)
self.forwarded_robot = False
# print("weighting matrix shape:",np.shape(self.w))
# print("Jacobian shape:",np.shape(self.J))
# print("desired vel shape:",np.shape(self.vel_des))
hessian = self.J.T.dot(self.w).dot(self.J)
# print hessian, "\n"
hessian += 1e-6 * np.identity(len(hessian))
gradient = -self.J.T.dot(self.w).dot(self.vel_des).reshape(-1)
w, v = np.linalg.eig(hessian)
# np.set_printoptions(precision=6)
# print w,"\n"
return self.qp_solver.quadprog_solve_qp(hessian, gradient)
class SecondOrderInverseKinematics(object):
def __init__(self, model, endeff_frame_names):
def getFrameId(name):
idx = self.robot.model.getFrameId(name)
if idx == len(self.robot.model.frames):
raise Exception('Unknown frame name: {}'.format(name))
return idx
self.robot = RobotWrapper(model)
self.robot_mass = sum([i.mass for i in self.robot.model.inertias[1:]])
self.base_id = getFrameId('base_link')
self.endeff_frame_names = endeff_frame_names
self.endeff_ids = [getFrameId(name) for name in endeff_frame_names]
self.is_init_time = True
self.ne = len(self.endeff_ids)
self.nv = self.robot.model.nv
# Tracking weights
self.w_endeff_tracking = 10**5
self.w_endeff_contact = 10**5
self.w_lin_mom_tracking = 100.0
self.w_ang_mom_tracking = 10.0
self.w_joint_regularization = 0.1
# P and D gains for tracking the position
self.p_endeff_tracking = 10000.
self.d_endeff_tracking = 200
self.p_com_tracking = 100.
self.p_orient_tracking = 10.
self.d_orient_tracking = 1.
self.p_mom_tracking = 10. * np.array([1., 1., 1., .01, .01, .01])
self.p_joint_regularization = 1.
self.d_joint_regularization = .5
self.desired_acceleration = np.zeros(
((self.ne + 2) * 3 + (self.nv - 6), ))
# Allocate space for the jacobian and desired velocities.
# Using two entires for the linear and angular velocity of the base.
# (self.nv - 6) is the number of joints for posture regularization
self.J = np.zeros(((self.ne + 2) * 3 + (self.nv - 6), self.nv))
self.drift_terms = np.zeros_like(
self.desired_acceleration) #i.e. dJ * dq
self.measured_velocities = np.zeros((self.J.shape[0], )) # i.e. J * dq
self.use_hierarchy = False
self.qp_solver = QpSolver()
def framesPos(self, frames):
"""
puts the translations of the list of frames in a len(frames) x 3 array
"""
return np.vstack([
self.robot.data.oMf[idx].translation for idx in frames
]).reshape([len(frames), 3])
def update_des_acc(self, q, dq, com_ref, orien_ref, mom_ref, dmom_ref,
endeff_pos_ref, endeff_vel_ref, endeff_acc_ref,
joint_regularization_ref):
measured_op_space_velocities = self.J @ dq
# get the ref momentum acc
self.desired_acceleration[:6] = dmom_ref + np.diag(
self.p_mom_tracking) @ (mom_ref - self.robot.data.hg)
# com part
self.desired_acceleration[:3] += self.p_com_tracking * (
com_ref - self.robot.com(q))
# orientation part
base_orien = self.robot.data.oMf[self.base_id].rotation
orient_error = pin.log3(
base_orien.T @ orien_ref.matrix()) # rotated in world
self.desired_acceleration[3:6] += (
self.p_orient_tracking * orient_error -
self.d_orient_tracking * dq[3:6])
# desired motion of the feet
for i, idx in enumerate(self.endeff_ids):
self.desired_acceleration[6 + 3 * i:6 + 3 *
(i + 1)] = self.p_endeff_tracking * (
endeff_pos_ref[i] -
self.robot.data.oMf[idx].translation)
self.desired_acceleration[6 + 3 * i:6 + 3 * (
i + 1)] += self.d_endeff_tracking * (
endeff_vel_ref[i] -
measured_op_space_velocities[6 + 3 * i:6 + 3 * (i + 1)])
self.desired_acceleration[6 + 3 * i:6 + 3 *
(i + 1)] += endeff_acc_ref[i]
if joint_regularization_ref is None:
self.desired_acceleration[(self.ne + 2) * 3:] = zero(self.nv - 6)
else:
# we add some damping
self.desired_acceleration[(self.ne + 2) *
3:] = self.p_joint_regularization * (
joint_regularization_ref - q[7:])
# REVIEW(mkhadiv): I am not sure if the negative sign makes sense here!
self.desired_acceleration[
(self.ne + 2) * 3:] += -self.d_joint_regularization * dq[6:]
def fill_weights(self, endeff_contact):
w = [
self.w_lin_mom_tracking * np.ones(3),
self.w_ang_mom_tracking * np.ones(3)
]
for eff in endeff_contact:
if eff == 1.: # If in contact
w.append(self.w_endeff_contact * np.ones(3))
else:
w.append(self.w_endeff_tracking * np.ones(3))
w.append(self.w_joint_regularization * np.ones(self.nv - 6))
self.w = np.diag(np.hstack(w))
def update_kinematics(self, q, dq):
# Update the pinocchio model.
self.robot.forwardKinematics(q, dq)
self.robot.computeJointJacobians(q)
self.robot.framesForwardKinematics(q)
pin.computeJointJacobiansTimeVariation(self.robot.model,
self.robot.data, q, dq)
pin.computeCentroidalMapTimeVariation(self.robot.model,
self.robot.data, q, dq)
# update the op space Jacobian
# the momentum Jacobian
self.J[:6, :] = self.robot.data.Ag
# the feet Jacobians
for i, idx in enumerate(self.endeff_ids):
self.J[6 + 3 * i:6 + 3 * (i + 1), :] = self.robot.getFrameJacobian(
idx, pin.ReferenceFrame.LOCAL_WORLD_ALIGNED)[:3]
# this is joint regularization part
self.J[(self.ne + 2) * 3:, 6:] = np.identity(self.nv - 6)
# update the dJdt dq component aka the drift
# the momentum drift
self.drift_terms[:6, ] = self.robot.data.dAg @ dq
# the feet drift
for i, idx in enumerate(self.endeff_ids):
self.drift_terms[
6 + 3 * i:6 + 3 *
(i + 1), ] = pin.getFrameJacobianTimeVariation(
self.robot.model, self.robot.data, idx,
pin.ReferenceFrame.LOCAL_WORLD_ALIGNED)[:3] @ dq
# note that the drift of the joints (as a task) is 0
def step(self, q, dq, com_ref, orien_ref, mom_ref, dmom_ref,
endeff_pos_ref, endeff_vel_ref, endeff_acc_ref, endeff_contact,
joint_regularization_ref):
'''
Arguments:
q: Current robot state
dq: Current robot velocity
com_ref: Reference com position in global coordinates
lmom_ref: Reference linear momentum in global coordinates
amom_ref: Reference angular momentum in global coordinates
endeff_pos_ref: [N_endeff x 3] Reference endeffectors position in global coordinates
endeff_vel_ref: [N_endeff x 3] Reference endeffectors velocity in global coordinates
'''
self.update_kinematics(q, dq)
self.update_des_acc(q, dq, com_ref, orien_ref, mom_ref, dmom_ref,
endeff_pos_ref, endeff_vel_ref, endeff_acc_ref,
joint_regularization_ref)
self.fill_weights(endeff_contact)
if self.use_hierarchy:
J_feet = self.J[6:6 + 12, :]
J_feet_pinv = scipy.linalg.pinv(J_feet, cond=0.00001)
ddq_feet = J_feet_pinv @ (self.desired_acceleration[6:6 + 12] -
self.drift_terms[6:6 + 12])
N_feet = np.eye(self.nv) - J_feet_pinv @ J_feet
J_rest = self.J[:6, :]
J_rest_pinv = scipy.linalg.pinv(J_rest @ N_feet, cond=0.00001)
rest_acc = self.desired_acceleration[:6]
rest_acc = rest_acc - self.drift_terms[:6]
rest_acc = rest_acc - J_rest @ ddq_feet
return ddq_feet + J_rest_pinv @ rest_acc
else:
hessian = self.J.T @ self.w @ self.J
hessian += 1e-6 * np.identity(len(hessian))
gradient = -self.J.T.dot(self.w).dot(self.desired_acceleration -
self.drift_terms).reshape(-1)
return self.qp_solver.quadprog_solve_qp(hessian, gradient)
def solve(self, dt, q_init, dq_init, com_ref, lmom_ref, amom_ref,
endeff_pos_ref, endeff_vel_ref, endeff_contact, joint_pos_ref,
base_ori_ref):
num_time_steps = com_ref.shape[0]
com_kin = np.zeros_like(com_ref)
lmom_kin = np.zeros_like(lmom_ref)
amom_kin = np.zeros_like(amom_ref)
endeff_pos_kin = np.zeros_like(endeff_pos_ref)
endeff_vel_kin = np.zeros_like(endeff_vel_ref)
q_kin = np.zeros([num_time_steps, q_init.shape[0]])
dq_kin = np.zeros([num_time_steps, dq_init.shape[0]])
ddq_kin = np.zeros_like(dq_kin)
inner_steps = int(dt / 0.001)
inner_dt = 0.001
time = np.linspace(0., (num_time_steps - 1) * dt, num_time_steps)
splined_com_ref = CubicSpline(time, com_ref)
splined_lmom_ref = CubicSpline(time, lmom_ref)
splined_amom_ref = CubicSpline(time, amom_ref)
splined_endeff_pos_ref = CubicSpline(time, endeff_pos_ref)
splined_endeff_vel_ref = CubicSpline(time, endeff_vel_ref)
splined_joint_pos_ref = CubicSpline(time, joint_pos_ref)
splined_base_ori_ref = CubicSpline(time, base_ori_ref)
# store the first one
q = q_init.copy()
dq = dq_init.copy()
self.update_kinematics(q, dq)
q_kin[0] = q
dq_kin[0] = dq
com_kin[0] = self.robot.com(q).T
hg = self.robot.centroidalMomentum(q, dq)
lmom_kin[0] = hg.linear.T
amom_kin[0] = hg.angular.T
endeff_pos_kin[0] = self.framesPos(self.endeff_ids)
endeff_vel_kin[0] = (self.J[6:(self.ne + 2) * 3].dot(dq).T).reshape(
[self.ne, 3])
dmom_ref = np.zeros([
6,
])
endeff_acc_ref = np.zeros([self.ne, 3])
t = 0.
for it in range(1, num_time_steps):
for inner in range(inner_steps):
dmom_ref = np.hstack(
(splined_lmom_ref(t, nu=1), splined_amom_ref(t, nu=1)))
endeff_acc_ref = splined_endeff_vel_ref(t, nu=1)
orien_ref = pin.Quaternion(
pin.rpy.rpyToMatrix(splined_base_ori_ref(t)))
ddq = self.step(
q, dq, splined_com_ref(t), orien_ref,
np.hstack((splined_lmom_ref(t), splined_amom_ref(t))),
dmom_ref, splined_endeff_pos_ref(t),
splined_endeff_vel_ref(t), endeff_acc_ref,
endeff_contact[it], splined_joint_pos_ref(t))
# Integrate to the next state.
dq += ddq * inner_dt
q = pin.integrate(self.robot.model, q, dq * inner_dt)
t += inner_dt
self.update_kinematics(q, dq)
q_kin[it] = q
dq_kin[it] = dq
ddq_kin[it] = ddq
com_kin[it] = self.robot.com(q).T
hg = self.robot.centroidalMomentum(q, dq)
lmom_kin[it] = hg.linear.T
amom_kin[it] = hg.angular.T
endeff_pos_kin[it] = self.framesPos(self.endeff_ids)
endeff_vel_kin[it] = (self.J[6:(self.ne + 2) *
3].dot(dq).T).reshape([self.ne, 3])
return q_kin, dq_kin, com_kin, lmom_kin, amom_kin, endeff_pos_kin, endeff_vel_kin