Move Pybullet Controllers + IK (#1170)

* Move Pybullet controllers and IK

- To their own respective modules
- Using PyCharm 'move' functionality

src/envs/pybullet_blocks.py

@@ -11,7 +11,7 @@
 from predicators.src.envs.blocks import BlocksEnv
 from predicators.src.envs.pybullet_env import PyBulletEnv, \
     create_pybullet_block
-from predicators.src.envs.pybullet_robots import \
+from predicators.src.pybullet_helpers.controllers import \
     create_change_fingers_option, create_move_end_effector_to_pose_option
 from predicators.src.pybullet_helpers.robots import SingleArmPyBulletRobot, \
     create_single_arm_pybullet_robot

src/envs/pybullet_cover.py

@@ -10,7 +10,7 @@
 from predicators.src.envs.cover import CoverEnv
 from predicators.src.envs.pybullet_env import PyBulletEnv, \
     create_pybullet_block
-from predicators.src.envs.pybullet_robots import \
+from predicators.src.pybullet_helpers.controllers import \
     create_change_fingers_option, create_move_end_effector_to_pose_option
 from predicators.src.pybullet_helpers.robots import SingleArmPyBulletRobot, \
     create_single_arm_pybullet_robot

src/envs/pybullet_robots.py

@@ -1,154 +1,19 @@
 """Interfaces to PyBullet robots."""
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Callable, Collection, Dict, Iterator, List, \
-    Optional, Sequence, Tuple, cast
+from typing import TYPE_CHECKING, Collection, Iterator, List, Optional, \
+    Sequence
 
 import numpy as np
 import pybullet as p
-from gym.spaces import Box
 
 from predicators.src import utils
 from predicators.src.settings import CFG
-from predicators.src.structs import Action, Array, JointsState, Object, \
-    ParameterizedOption, Pose3D, State, Type
+from predicators.src.structs import JointsState
 
 if TYPE_CHECKING:
     from predicators.src.pybullet_helpers.robots import SingleArmPyBulletRobot
 
-################################# Controllers #################################
-
-
-def create_move_end_effector_to_pose_option(
-    robot: SingleArmPyBulletRobot,
-    name: str,
-    types: Sequence[Type],
-    params_space: Box,
-    get_current_and_target_pose_and_finger_status: Callable[
-        [State, Sequence[Object], Array], Tuple[Pose3D, Pose3D, str]],
-    move_to_pose_tol: float,
-    max_vel_norm: float,
-    finger_action_nudge_magnitude: float,
-) -> ParameterizedOption:
-    """A generic utility that creates a ParameterizedOption for moving the end
-    effector to a target pose, given a function that takes in the current
-    state, objects, and parameters, and returns the current pose and target
-    pose of the end effector, and the finger status."""
-
-    assert robot.get_name() == "fetch", "Move end effector to pose option " + \
-        f"not implemented for robot {robot.get_name()}."
-
-    def _policy(state: State, memory: Dict, objects: Sequence[Object],
-                params: Array) -> Action:
-        del memory  # unused
-        # First handle the main arm joints.
-        current, target, finger_status = \
-            get_current_and_target_pose_and_finger_status(
-                state, objects, params)
-        # Run IK to determine the target joint positions.
-        ee_delta = np.subtract(target, current)
-        # Reduce the target to conform to the max velocity constraint.
-        ee_norm = np.linalg.norm(ee_delta)
-        if ee_norm > max_vel_norm:
-            ee_delta = ee_delta * max_vel_norm / ee_norm
-        ee_action = np.add(current, ee_delta)
-        # Keep validate as False because validate=True would update the
-        # state of the robot during simulation, which overrides physics.
-        joints_state = robot.inverse_kinematics(
-            (ee_action[0], ee_action[1], ee_action[2]), validate=False)
-        # Handle the fingers. Fingers drift if left alone.
-        # When the fingers are not explicitly being opened or closed, we
-        # nudge the fingers toward being open or closed according to the
-        # finger status.
-        if finger_status == "open":
-            finger_delta = finger_action_nudge_magnitude
-        else:
-            assert finger_status == "closed"
-            finger_delta = -finger_action_nudge_magnitude
-        # Extract the current finger state.
-        state = cast(utils.PyBulletState, state)
-        finger_state = state.joints_state[robot.left_finger_joint_idx]
-        # The finger action is an absolute joint position for the fingers.
-        f_action = finger_state + finger_delta
-        # Override the meaningless finger values in joint_action.
-        joints_state[robot.left_finger_joint_idx] = f_action
-        joints_state[robot.right_finger_joint_idx] = f_action
-        action_arr = np.array(joints_state, dtype=np.float32)
-        # This clipping is needed sometimes for the joint limits.
-        action_arr = np.clip(action_arr, robot.action_space.low,
-                             robot.action_space.high)
-        assert robot.action_space.contains(action_arr)
-        return Action(action_arr)
-
-    def _terminal(state: State, memory: Dict, objects: Sequence[Object],
-                  params: Array) -> bool:
-        del memory  # unused
-        current, target, _ = \
-            get_current_and_target_pose_and_finger_status(
-                state, objects, params)
-        squared_dist = np.sum(np.square(np.subtract(current, target)))
-        return squared_dist < move_to_pose_tol
-
-    return ParameterizedOption(name,
-                               types=types,
-                               params_space=params_space,
-                               policy=_policy,
-                               initiable=lambda _1, _2, _3, _4: True,
-                               terminal=_terminal)
-
-
-def create_change_fingers_option(
-    robot: SingleArmPyBulletRobot,
-    name: str,
-    types: Sequence[Type],
-    params_space: Box,
-    get_current_and_target_val: Callable[[State, Sequence[Object], Array],
-                                         Tuple[float, float]],
-    max_vel_norm: float,
-    grasp_tol: float,
-) -> ParameterizedOption:
-    """A generic utility that creates a ParameterizedOption for changing the
-    robot fingers, given a function that takes in the current state, objects,
-    and parameters, and returns the current and target finger joint values."""
-
-    assert robot.get_name() == "fetch", "Change fingers option not " + \
-        f"implemented for robot {robot.get_name()}."
-
-    def _policy(state: State, memory: Dict, objects: Sequence[Object],
-                params: Array) -> Action:
-        del memory  # unused
-        current_val, target_val = get_current_and_target_val(
-            state, objects, params)
-        f_delta = target_val - current_val
-        f_delta = np.clip(f_delta, -max_vel_norm, max_vel_norm)
-        f_action = current_val + f_delta
-        # Don't change the rest of the joints.
-        state = cast(utils.PyBulletState, state)
-        target = np.array(state.joints_state, dtype=np.float32)
-        target[robot.left_finger_joint_idx] = f_action
-        target[robot.right_finger_joint_idx] = f_action
-        # This clipping is needed sometimes for the joint limits.
-        target = np.clip(target, robot.action_space.low,
-                         robot.action_space.high)
-        assert robot.action_space.contains(target)
-        return Action(target)
-
-    def _terminal(state: State, memory: Dict, objects: Sequence[Object],
-                  params: Array) -> bool:
-        del memory  # unused
-        current_val, target_val = get_current_and_target_val(
-            state, objects, params)
-        squared_dist = (target_val - current_val)**2
-        return squared_dist < grasp_tol
-
-    return ParameterizedOption(name,
-                               types=types,
-                               params_space=params_space,
-                               policy=_policy,
-                               initiable=lambda _1, _2, _3, _4: True,
-                               terminal=_terminal)
-
-
 ########################### Other utility functions ###########################
 
 
@@ -169,89 +34,6 @@ def get_kinematic_chain(robot: int, end_effector: int,
     return kinematic_chain
 
 
-def pybullet_inverse_kinematics(
-    robot: int,
-    end_effector: int,
-    target_position: Pose3D,
-    target_orientation: Sequence[float],
-    joints: Sequence[int],
-    physics_client_id: int,
-    validate: bool = True,
-) -> JointsState:
-    """Runs IK and returns a joints state for the given (free) joints.
-
-    If validate is True, the PyBullet IK solver is called multiple
-    times, resetting the robot state each time, until the target
-    position is reached. If the target position is not reached after a
-    maximum number of iters, an exception is raised.
-    """
-    # Figure out which joint each dimension of the return of IK corresponds to.
-    free_joints = []
-    num_joints = p.getNumJoints(robot, physicsClientId=physics_client_id)
-    for idx in range(num_joints):
-        joint_info = p.getJointInfo(robot,
-                                    idx,
-                                    physicsClientId=physics_client_id)
-        if joint_info[3] > -1:
-            free_joints.append(idx)
-    assert set(joints).issubset(set(free_joints))
-
-    # Record the initial state of the joints so that we can reset them after.
-    if validate:
-        initial_joints_states = p.getJointStates(
-            robot, free_joints, physicsClientId=physics_client_id)
-        assert len(initial_joints_states) == len(free_joints)
-
-    # Running IK once is often insufficient, so we run it multiple times until
-    # convergence. If it does not converge, an error is raised.
-    convergence_tol = CFG.pybullet_ik_tol
-    for _ in range(CFG.pybullet_max_ik_iters):
-        free_joint_vals = p.calculateInverseKinematics(
-            robot,
-            end_effector,
-            target_position,
-            targetOrientation=target_orientation,
-            physicsClientId=physics_client_id)
-        assert len(free_joints) == len(free_joint_vals)
-        if not validate:
-            break
-        # Update the robot state and check if the desired position and
-        # orientation are reached.
-        for joint, joint_val in zip(free_joints, free_joint_vals):
-            p.resetJointState(robot,
-                              joint,
-                              targetValue=joint_val,
-                              physicsClientId=physics_client_id)
-        ee_link_state = p.getLinkState(robot,
-                                       end_effector,
-                                       computeForwardKinematics=True,
-                                       physicsClientId=physics_client_id)
-        position = ee_link_state[4]
-        # Note: we are checking positions only for convergence.
-        if np.allclose(position, target_position, atol=convergence_tol):
-            break
-    else:
-        raise Exception("Inverse kinematics failed to converge.")
-
-    # Reset the joint states to their initial values to avoid modifying the
-    # PyBullet internal state.
-    if validate:
-        for joint, (pos, vel, _, _) in zip(free_joints, initial_joints_states):
-            p.resetJointState(robot,
-                              joint,
-                              targetValue=pos,
-                              targetVelocity=vel,
-                              physicsClientId=physics_client_id)
-    # Order the found free_joint_vals based on the requested joints.
-    joint_vals = []
-    for joint in joints:
-        free_joint_idx = free_joints.index(joint)
-        joint_val = free_joint_vals[free_joint_idx]
-        joint_vals.append(joint_val)
-
-    return joint_vals
-
-
 def run_motion_planning(
         robot: SingleArmPyBulletRobot, initial_state: JointsState,
         target_state: JointsState, collision_bodies: Collection[int],