def controller(self, obs: dict):
        # Tolerances on reaching keyframes
        posePosTol = 0.01
        poseAxangTol = 0.09

        # Which keyframe are we in?
        if self.poseNum == 0:
            rob0GoalPos = self.p_rob0_1
            rob1GoalPos = self.p_rob1_1
            rob0GoalAxAng = self.axang_rob0_1
            rob1GoalAxAng = self.axang_rob1_1
        elif self.poseNum == 1:
            rob0GoalPos = self.p_rob0_2
            rob1GoalPos = self.p_rob1_2
            rob0GoalAxAng = self.axang_rob0_2
            rob1GoalAxAng = self.axang_rob1_2
        elif self.poseNum >= 2:
            rob0GoalPos = self.p_rob0_3
            rob1GoalPos = self.p_rob1_3
            rob0GoalAxAng = self.axang_rob0_3
            rob1GoalAxAng = self.axang_rob1_3

        rob0OrientationError = T.get_orientation_error(
            obs["robot0_eef_quat"], T.axisangle2quat(rob0GoalAxAng))
        rob1OrientationError = T.get_orientation_error(
            obs["robot1_eef_quat"], T.axisangle2quat(rob1GoalAxAng))

        # Absolute pose control, so actions are just the poses we want to get to
        posActionRob0 = self.p_rob0_1
        posActionRob1 = self.p_rob1_1
        axangActionRob0 = self.axang_rob0_1
        axangActionRob1 = self.axang_rob1_1

        # If we have reached the next keyframe, increment pose counter
        if np.linalg.norm(
                posActionRob0 / self.kpp) < posePosTol and np.linalg.norm(
                    rob0OrientationError) < poseAxangTol and np.linalg.norm(
                        posActionRob1 /
                        self.kpp) < posePosTol and np.linalg.norm(
                            rob1OrientationError) < poseAxangTol:
            if self.poseNum <= 1:
                self.poseNum = self.poseNum + 1
            elif self.poseNum == 2:
                print("done!")
                self.poseNum = 3

        return np.hstack((posActionRob0, axangActionRob0, posActionRob1,
                          axangActionRob1)).tolist()
Ejemplo n.º 2
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    def _clip_ik_input(self, dpos, rotation):
        """
        Helper function that clips desired ik input deltas into a valid range.

        Args:
            dpos (np.array): a 3 dimensional array corresponding to the desired
                change in x, y, and z end effector position.
            rotation (np.array): relative rotation in scaled axis angle form (ax, ay, az)
                corresponding to the (relative) desired orientation of the end effector.

        Returns:
            2-tuple:

                - (np.array) clipped dpos
                - (np.array) clipped rotation
        """
        # scale input range to desired magnitude
        if dpos.any():
            dpos, _ = T.clip_translation(dpos, self.ik_pos_limit)

        # Map input to quaternion
        rotation = T.axisangle2quat(rotation)

        # Clip orientation to desired magnitude
        rotation, _ = T.clip_rotation(rotation, self.ik_ori_limit)

        return dpos, rotation
Ejemplo n.º 3
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    def _randomize_direction(self, name):
        """
        Helper function to randomize direction of a specific light source

        Args:
            name (str): Name of the lighting source to randomize for
        """
        # sample a small, random axis-angle delta rotation
        random_axis, random_angle = trans.random_axis_angle(angle_limit=self.direction_perturbation_size, random_state=self.random_state)
        random_delta_rot = trans.quat2mat(trans.axisangle2quat(random_axis * random_angle))
        
        # rotate direction by this delta rotation and set the new direction
        new_dir = random_delta_rot.dot(self._defaults[name]['dir'])
        self.set_dir(
            name,
            new_dir,
        )
Ejemplo n.º 4
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    def _randomize_rotation(self, name):
        """
        Helper function to randomize orientation of a specific camera

        Args:
            name (str): Name of the camera to randomize for
        """
        # sample a small, random axis-angle delta rotation
        random_axis, random_angle = trans.random_axis_angle(angle_limit=self.rotation_perturbation_size, random_state=self.random_state)
        random_delta_rot = trans.quat2mat(trans.axisangle2quat(random_axis * random_angle))
        
        # compute new rotation and set it
        base_rot = trans.quat2mat(trans.convert_quat(self._defaults[name]['quat'], to='xyzw'))
        new_rot = random_delta_rot.T.dot(base_rot)
        new_quat = trans.convert_quat(trans.mat2quat(new_rot), to='wxyz')
        self.set_quat(
            name,
            new_quat,
        )
Ejemplo n.º 5
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    def set_goal(self, action, set_pos=None, set_ori=None):
        """
        Sets goal based on input @action. If self.impedance_mode is not "fixed", then the input will be parsed into the
        delta values to update the goal position / pose and the kp and/or damping_ratio values to be immediately updated
        internally before executing the proceeding control loop.

        Note that @action expected to be in the following format, based on impedance mode!

            :Mode `'fixed'`: [joint pos command]
            :Mode `'variable'`: [damping_ratio values, kp values, joint pos command]
            :Mode `'variable_kp'`: [kp values, joint pos command]

        Args:
            action (Iterable): Desired relative joint position goal state
            set_pos (Iterable): If set, overrides @action and sets the desired absolute eef position goal state
            set_ori (Iterable): IF set, overrides @action and sets the desired absolute eef orientation goal state
        """
        # Update state
        self.update()

        # Parse action based on the impedance mode, and update kp / kd as necessary
        if self.impedance_mode == "variable":
            damping_ratio, kp, delta = action[:6], action[6:12], action[12:]
            self.kp = np.clip(kp, self.kp_min, self.kp_max)
            self.kd = 2 * np.sqrt(self.kp) * np.clip(
                damping_ratio, self.damping_ratio_min, self.damping_ratio_max)
        elif self.impedance_mode == "variable_kp":
            kp, delta = action[:6], action[6:]
            self.kp = np.clip(kp, self.kp_min, self.kp_max)
            self.kd = 2 * np.sqrt(self.kp)  # critically damped
        else:  # This is case "fixed"
            delta = action

        # If we're using deltas, interpret actions as such
        if self.use_delta:
            if delta is not None:
                scaled_delta = self.scale_action(delta)
                if not self.use_ori:
                    # Set default control for ori since user isn't actively controlling ori
                    set_ori = np.array([[-1., 0., 0.], [0., 1., 0.],
                                        [0., 0., -1.]])
            else:
                scaled_delta = []
        # Else, interpret actions as absolute values
        else:
            set_pos = delta[:3]
            # Set default control for ori if we're only using position control
            set_ori = T.quat2mat(T.axisangle2quat(delta[3:6])) if self.use_ori else \
                np.array([[-1., 0., 0.], [0., 1., 0.], [0., 0., -1.]])
            scaled_delta = []

        # We only want to update goal orientation if there is a valid delta ori value
        # use math.isclose instead of numpy because numpy is slow
        bools = [
            0. if math.isclose(elem, 0.) else 1. for elem in scaled_delta[3:]
        ]
        if sum(bools) > 0.:
            self.goal_ori = set_goal_orientation(
                scaled_delta[3:],
                self.ee_ori_mat,
                orientation_limit=self.orientation_limits,
                set_ori=set_ori)
        self.goal_pos = set_goal_position(scaled_delta[:3],
                                          self.ee_pos,
                                          position_limit=self.position_limits,
                                          set_pos=set_pos)

        if self.interpolator_pos is not None:
            self.interpolator_pos.set_goal(self.goal_pos)

        if self.interpolator_ori is not None:
            self.ori_ref = np.array(
                self.ee_ori_mat
            )  # reference is the current orientation at start
            self.interpolator_ori.set_goal(
                orientation_error(
                    self.goal_ori,
                    self.ori_ref))  # goal is the total orientation error
            self.relative_ori = np.zeros(
                3)  # relative orientation always starts at 0
Ejemplo n.º 6
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    def _get_geom_attrs(self):
        """
        Creates geom elements that will be passed to superclass CompositeObject constructor

        Returns:
            dict: args to be used by CompositeObject to generate geoms
        """
        full_size = np.array((
            self.body_half_size,
            self.body_half_size + self.handle_length * 2,
            self.body_half_size,
        ))
        # Initialize dict of obj args that we'll pass to the CompositeObject constructor
        base_args = {
            "total_size": full_size / 2.0,
            "name": self.name,
            "locations_relative_to_center": True,
            "obj_types": "all",
        }
        site_attrs = []
        obj_args = {}

        # Initialize geom lists
        self._handle0_geoms = []
        self._handle1_geoms = []

        # Add main pot body
        # Base geom
        name = f"base"
        self.pot_base = [name]
        add_to_dict(
            dic=obj_args,
            geom_types="box",
            geom_locations=(0, 0,
                            -self.body_half_size[2] + self.thickness / 2),
            geom_quats=(1, 0, 0, 0),
            geom_sizes=np.array([
                self.body_half_size[0], self.body_half_size[1],
                self.thickness / 2
            ]),
            geom_names=name,
            geom_rgbas=None if self.use_texture else self.rgba_body,
            geom_materials="pot_mat" if self.use_texture else None,
            geom_frictions=None,
            density=self.density,
        )

        # Walls
        x_off = np.array([
            0, -(self.body_half_size[0] - self.thickness / 2), 0,
            self.body_half_size[0] - self.thickness / 2
        ])
        y_off = np.array([
            -(self.body_half_size[1] - self.thickness / 2), 0,
            self.body_half_size[1] - self.thickness / 2, 0
        ])
        w_vals = np.array([
            self.body_half_size[1], self.body_half_size[0],
            self.body_half_size[1], self.body_half_size[0]
        ])
        r_vals = np.array([np.pi / 2, 0, -np.pi / 2, np.pi])
        for i, (x, y, w, r) in enumerate(zip(x_off, y_off, w_vals, r_vals)):
            add_to_dict(
                dic=obj_args,
                geom_types="box",
                geom_locations=(x, y, 0),
                geom_quats=T.convert_quat(T.axisangle2quat(np.array([0, 0,
                                                                     r])),
                                          to="wxyz"),
                geom_sizes=np.array(
                    [self.thickness / 2, w, self.body_half_size[2]]),
                geom_names=f"body{i}",
                geom_rgbas=None if self.use_texture else self.rgba_body,
                geom_materials="pot_mat" if self.use_texture else None,
                geom_frictions=None,
                density=self.density,
            )

        # Add handles
        main_bar_size = np.array([
            self.handle_width / 2 + self.handle_radius,
            self.handle_radius,
            self.handle_radius,
        ])
        side_bar_size = np.array(
            [self.handle_radius, self.handle_length / 2, self.handle_radius])
        handle_z = self.body_half_size[2] - self.handle_radius
        for i, (g_list, handle_side, rgba) in enumerate(
                zip([self._handle0_geoms, self._handle1_geoms], [1.0, -1.0],
                    [self.rgba_handle_0, self.rgba_handle_1])):
            handle_center = np.array(
                (0,
                 handle_side * (self.body_half_size[1] + self.handle_length),
                 handle_z))
            # Solid handle case
            if self.solid_handle:
                name = f"handle{i}"
                g_list.append(name)
                add_to_dict(
                    dic=obj_args,
                    geom_types="box",
                    geom_locations=handle_center,
                    geom_quats=(1, 0, 0, 0),
                    geom_sizes=np.array([
                        self.handle_width / 2, self.handle_length / 2,
                        self.handle_radius
                    ]),
                    geom_names=name,
                    geom_rgbas=None if self.use_texture else rgba,
                    geom_materials=f"handle{i}_mat"
                    if self.use_texture else None,
                    geom_frictions=(self.handle_friction, 0.005, 0.0001),
                    density=self.density,
                )
            # Hollow handle case
            else:
                # Center bar
                name = f"handle{i}_c"
                g_list.append(name)
                add_to_dict(
                    dic=obj_args,
                    geom_types="box",
                    geom_locations=handle_center,
                    geom_quats=(1, 0, 0, 0),
                    geom_sizes=main_bar_size,
                    geom_names=name,
                    geom_rgbas=None if self.use_texture else rgba,
                    geom_materials=f"handle{i}_mat"
                    if self.use_texture else None,
                    geom_frictions=(self.handle_friction, 0.005, 0.0001),
                    density=self.density,
                )
                # Side bars
                for bar_side, suffix in zip([-1.0, 1.0], ["-", "+"]):
                    name = f"handle{i}_{suffix}"
                    g_list.append(name)
                    add_to_dict(
                        dic=obj_args,
                        geom_types="box",
                        geom_locations=(
                            bar_side * self.handle_width / 2,
                            handle_side *
                            (self.body_half_size[1] + self.handle_length / 2),
                            handle_z,
                        ),
                        geom_quats=(1, 0, 0, 0),
                        geom_sizes=side_bar_size,
                        geom_names=name,
                        geom_rgbas=None if self.use_texture else rgba,
                        geom_materials=f"handle{i}_mat"
                        if self.use_texture else None,
                        geom_frictions=(self.handle_friction, 0.005, 0.0001),
                        density=self.density,
                    )
            # Add relevant site
            handle_site = self.get_site_attrib_template()
            handle_name = f"handle{i}"
            handle_site.update({
                "name":
                handle_name,
                "pos":
                array_to_string(handle_center -
                                handle_side * np.array([0, 0.005, 0])),
                "size":
                "0.005",
                "rgba":
                rgba,
            })
            site_attrs.append(handle_site)
            # Add to important sites
            self._important_sites[
                f"handle{i}"] = self.naming_prefix + handle_name

        # Add pot body site
        pot_site = self.get_site_attrib_template()
        center_name = "center"
        pot_site.update({
            "name": center_name,
            "size": "0.005",
        })
        site_attrs.append(pot_site)
        # Add to important sites
        self._important_sites["center"] = self.naming_prefix + center_name

        # Add back in base args and site args
        obj_args.update(base_args)
        obj_args["sites"] = site_attrs  # All sites are part of main (top) body

        # Return this dict
        return obj_args
Ejemplo n.º 7
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def set_goal_orientation(delta,
                         current_orientation,
                         orientation_limit=None,
                         set_ori=None):
    """
    Calculates and returns the desired goal orientation, clipping the result accordingly to @orientation_limits.
    @delta and @current_orientation must be specified if a relative goal is requested, else @set_ori must be
    an orientation matrix specified to define a global orientation

    Args:
        delta (np.array): Desired relative change in orientation, in axis-angle form [ax, ay, az]
        current_orientation (np.array): Current orientation, in rotation matrix form
        orientation_limit (None or np.array): 2d array defining the (min, max) limits of permissible orientation goal commands
        set_ori (None or np.array): If set, will ignore @delta and set the goal orientation to this value

    Returns:
        np.array: calculated goal orientation in absolute coordinates

    Raises:
        ValueError: [Invalid orientation_limit shape]
    """
    # directly set orientation
    if set_ori is not None:
        goal_orientation = set_ori

    # otherwise use delta to set goal orientation
    else:
        # convert axis-angle value to rotation matrix
        quat_error = trans.axisangle2quat(delta)
        rotation_mat_error = trans.quat2mat(quat_error)
        goal_orientation = np.dot(rotation_mat_error, current_orientation)

    # check for orientation limits
    if np.array(orientation_limit).any():
        if orientation_limit.shape != (2, 3):
            raise ValueError("Orientation limit should be shaped (2,3) "
                             "but is instead: {}".format(
                                 orientation_limit.shape))

        # Convert to euler angles for clipping
        euler = trans.mat2euler(goal_orientation)

        # Clip euler angles according to specified limits
        limited = False
        for idx in range(3):
            if orientation_limit[0][idx] < orientation_limit[1][
                    idx]:  # Normal angle sector meaning
                if orientation_limit[0][idx] < euler[idx] < orientation_limit[
                        1][idx]:
                    continue
                else:
                    limited = True
                    dist_to_lower = euler[idx] - orientation_limit[0][idx]
                    if dist_to_lower > np.pi:
                        dist_to_lower -= 2 * np.pi
                    elif dist_to_lower < -np.pi:
                        dist_to_lower += 2 * np.pi

                    dist_to_higher = euler[idx] - orientation_limit[1][idx]
                    if dist_to_lower > np.pi:
                        dist_to_higher -= 2 * np.pi
                    elif dist_to_lower < -np.pi:
                        dist_to_higher += 2 * np.pi

                    if dist_to_lower < dist_to_higher:
                        euler[idx] = orientation_limit[0][idx]
                    else:
                        euler[idx] = orientation_limit[1][idx]
            else:  # Inverted angle sector meaning
                if orientation_limit[0][idx] < euler[idx] or euler[
                        idx] < orientation_limit[1][idx]:
                    continue
                else:
                    limited = True
                    dist_to_lower = euler[idx] - orientation_limit[0][idx]
                    if dist_to_lower > np.pi:
                        dist_to_lower -= 2 * np.pi
                    elif dist_to_lower < -np.pi:
                        dist_to_lower += 2 * np.pi

                    dist_to_higher = euler[idx] - orientation_limit[1][idx]
                    if dist_to_lower > np.pi:
                        dist_to_higher -= 2 * np.pi
                    elif dist_to_lower < -np.pi:
                        dist_to_higher += 2 * np.pi

                    if dist_to_lower < dist_to_higher:
                        euler[idx] = orientation_limit[0][idx]
                    else:
                        euler[idx] = orientation_limit[1][idx]
        if limited:
            goal_orientation = trans.euler2mat(
                np.array([euler[0], euler[1], euler[2]]))
    return goal_orientation