working bp version
This commit is contained in:
Onur
@@ -0,0 +1,17 @@
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from mp_pytorch import PhaseGenerator, NormalizedRBFBasisGenerator, ZeroStartNormalizedRBFBasisGenerator
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from mp_pytorch.basis_gn.rhytmic_basis import RhythmicBasisGenerator
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ALL_TYPES = ["rbf", "zero_rbf", "rhythmic"]
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def get_basis_generator(basis_generator_type: str, phase_generator: PhaseGenerator, **kwargs):
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basis_generator_type = basis_generator_type.lower()
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if basis_generator_type == "rbf":
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return NormalizedRBFBasisGenerator(phase_generator, **kwargs)
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elif basis_generator_type == "zero_rbf":
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return ZeroStartNormalizedRBFBasisGenerator(phase_generator, **kwargs)
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elif basis_generator_type == "rhythmic":
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return RhythmicBasisGenerator(phase_generator, **kwargs)
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else:
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raise ValueError(f"Specified basis generator type {basis_generator_type} not supported, "
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f"please choose one of {ALL_TYPES}.")
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@@ -0,0 +1,4 @@
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class BaseController:
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def get_action(self, des_pos, des_vel, c_pos, c_vel):
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raise NotImplementedError
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@@ -0,0 +1,21 @@
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from alr_envs.mp.controllers.meta_world_controller import MetaWorldController
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from alr_envs.mp.controllers.pd_controller import PDController
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from alr_envs.mp.controllers.vel_controller import VelController
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from alr_envs.mp.controllers.pos_controller import PosController
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ALL_TYPES = ["motor", "velocity", "position", "metaworld"]
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def get_controller(controller_type: str, **kwargs):
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controller_type = controller_type.lower()
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if controller_type == "motor":
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return PDController(**kwargs)
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elif controller_type == "velocity":
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return VelController()
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elif controller_type == "position":
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return PosController()
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elif controller_type == "metaworld":
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return MetaWorldController()
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else:
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raise ValueError(f"Specified controller type {controller_type} not supported, "
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f"please choose one of {ALL_TYPES}.")
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@@ -0,0 +1,25 @@
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import numpy as np
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from alr_envs.mp.controllers.base_controller import BaseController
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class MetaWorldController(BaseController):
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"""
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A Metaworld Controller. Using position and velocity information from a provided environment,
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the controller calculates a response based on the desired position and velocity.
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Unlike the other Controllers, this is a special controller for MetaWorld environments.
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They use a position delta for the xyz coordinates and a raw position for the gripper opening.
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:param env: A position environment
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"""
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def get_action(self, des_pos, des_vel, c_pos, c_vel):
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gripper_pos = des_pos[-1]
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cur_pos = env.current_pos[:-1]
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xyz_pos = des_pos[:-1]
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assert xyz_pos.shape == cur_pos.shape, \
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f"Mismatch in dimension between desired position {xyz_pos.shape} and current position {cur_pos.shape}"
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trq = np.hstack([(xyz_pos - cur_pos), gripper_pos])
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return trq
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@@ -0,0 +1,28 @@
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from typing import Union, Tuple
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from alr_envs.mp.controllers.base_controller import BaseController
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class PDController(BaseController):
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"""
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A PD-Controller. Using position and velocity information from a provided environment,
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the controller calculates a response based on the desired position and velocity
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:param env: A position environment
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:param p_gains: Factors for the proportional gains
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:param d_gains: Factors for the differential gains
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"""
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def __init__(self,
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p_gains: Union[float, Tuple] = 1,
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d_gains: Union[float, Tuple] = 0.5):
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self.p_gains = p_gains
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self.d_gains = d_gains
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def get_action(self, des_pos, des_vel, c_pos, c_vel):
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assert des_pos.shape == c_pos.shape, \
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f"Mismatch in dimension between desired position {des_pos.shape} and current position {c_pos.shape}"
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assert des_vel.shape == c_vel.shape, \
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f"Mismatch in dimension between desired velocity {des_vel.shape} and current velocity {c_vel.shape}"
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trq = self.p_gains * (des_pos - c_pos) + self.d_gains * (des_vel - c_vel)
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return trq
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@@ -0,0 +1,9 @@
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from alr_envs.mp.controllers.base_controller import BaseController
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class PosController(BaseController):
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"""
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A Position Controller. The controller calculates a response only based on the desired position.
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"""
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def get_action(self, des_pos, des_vel, c_pos, c_vel):
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return des_pos
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@@ -0,0 +1,9 @@
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from alr_envs.mp.controllers.base_controller import BaseController
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class VelController(BaseController):
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"""
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A Velocity Controller. The controller calculates a response only based on the desired velocity.
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"""
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def get_action(self, des_pos, des_vel, c_pos, c_vel):
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return des_vel
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@@ -0,0 +1,236 @@
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from abc import ABC, abstractmethod
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from typing import Union, Tuple
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import gym
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import numpy as np
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from gym import spaces
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from mp_pytorch.mp.mp_interfaces import MPInterface
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from alr_envs.mp.controllers.base_controller import BaseController
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class EpisodicWrapper(gym.Env, ABC):
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"""
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Base class for movement primitive based gym.Wrapper implementations.
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Args:
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env: The (wrapped) environment this wrapper is applied on
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num_dof: Dimension of the action space of the wrapped env
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num_basis: Number of basis functions per dof
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duration: Length of the trajectory of the movement primitive in seconds
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controller: Type or object defining the policy that is used to generate action based on the trajectory
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weight_scale: Scaling parameter for the actions given to this wrapper
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render_mode: Equivalent to gym render mode
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"""
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def __init__(self,
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env: gym.Env,
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mp: MPInterface,
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controller: BaseController,
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duration: float,
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render_mode: str = None,
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verbose: int = 1,
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weight_scale: float = 1):
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super().__init__()
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self.env = env
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try:
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self.dt = env.dt
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except AttributeError:
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raise AttributeError("step based environment needs to have a function 'dt' ")
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self.duration = duration
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self.traj_steps = int(duration / self.dt)
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self.post_traj_steps = self.env.spec.max_episode_steps - self.traj_steps
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self.controller = controller
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self.mp = mp
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self.env = env
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self.verbose = verbose
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self.weight_scale = weight_scale
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# rendering
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self.render_mode = render_mode
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self.render_kwargs = {}
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# self.time_steps = np.linspace(0, self.duration, self.traj_steps + 1)
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self.time_steps = np.linspace(0, self.duration, self.traj_steps)
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self.mp.set_mp_times(self.time_steps)
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# action_bounds = np.inf * np.ones((np.prod(self.mp.num_params)))
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min_action_bounds, max_action_bounds = mp.get_param_bounds()
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self.mp_action_space = gym.spaces.Box(low=min_action_bounds.numpy(), high=max_action_bounds.numpy(),
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dtype=np.float32)
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self.action_space = self.set_action_space()
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self.active_obs = self.set_active_obs()
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self.observation_space = spaces.Box(low=self.env.observation_space.low[self.active_obs],
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high=self.env.observation_space.high[self.active_obs],
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dtype=self.env.observation_space.dtype)
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def get_trajectory(self, action: np.ndarray) -> Tuple:
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# TODO: this follows the implementation of the mp_pytorch library which includes the paramters tau and delay at
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# the beginning of the array.
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ignore_indices = int(self.mp.learn_tau) + int(self.mp.learn_delay)
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scaled_mp_params = action.copy()
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scaled_mp_params[ignore_indices:] *= self.weight_scale
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self.mp.set_params(scaled_mp_params)
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self.mp.set_boundary_conditions(bc_time=self.time_steps[:1], bc_pos=self.current_pos, bc_vel=self.current_vel)
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traj_dict = self.mp.get_mp_trajs(get_pos = True, get_vel = True)
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trajectory_tensor, velocity_tensor = traj_dict['pos'], traj_dict['vel']
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trajectory = trajectory_tensor.numpy()
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velocity = velocity_tensor.numpy()
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if self.post_traj_steps > 0:
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trajectory = np.vstack([trajectory, np.tile(trajectory[-1, :], [self.post_traj_steps, 1])])
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velocity = np.vstack([velocity, np.zeros(shape=(self.post_traj_steps, self.mp.num_dof))])
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return trajectory, velocity
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def set_action_space(self):
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"""
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This function can be used to modify the action space for considering actions which are not learned via motion
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primitives. E.g. ball releasing time for the beer pong task. By default, it is the parameter space of the
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motion primitive.
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Only needs to be overwritten if the action space needs to be modified.
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"""
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return self.mp_action_space
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def _episode_callback(self, action: np.ndarray) -> Tuple[np.ndarray, Union[np.ndarray, None]]:
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"""
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Used to extract the parameters for the motion primitive and other parameters from an action array which might
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include other actions like ball releasing time for the beer pong environment.
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This only needs to be overwritten if the action space is modified.
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Args:
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action: a vector instance of the whole action space, includes mp parameters and additional parameters if
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specified, else only mp parameters
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Returns:
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Tuple: mp_arguments and other arguments
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"""
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return action, None
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def _step_callback(self, t: int, env_spec_params: Union[np.ndarray, None], step_action: np.ndarray) -> Union[np.ndarray]:
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"""
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This function can be used to modify the step_action with additional parameters e.g. releasing the ball in the
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Beerpong env. The parameters used should not be part of the motion primitive parameters.
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Returns step_action by default, can be overwritten in individual mp_wrappers.
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Args:
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t: the current time step of the episode
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env_spec_params: the environment specific parameter, as defined in fucntion _episode_callback
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(e.g. ball release time in Beer Pong)
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step_action: the current step-based action
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Returns:
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modified step action
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"""
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return step_action
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@abstractmethod
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def set_active_obs(self) -> np.ndarray:
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"""
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This function defines the contexts. The contexts are defined as specific observations.
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Returns:
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boolearn array representing the indices of the observations
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"""
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return np.ones(self.env.observation_space.shape[0], dtype=bool)
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@property
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@abstractmethod
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def current_pos(self) -> Union[float, int, np.ndarray, Tuple]:
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"""
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Returns the current position of the action/control dimension.
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The dimensionality has to match the action/control dimension.
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This is not required when exclusively using velocity control,
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it should, however, be implemented regardless.
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E.g. The joint positions that are directly or indirectly controlled by the action.
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"""
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raise NotImplementedError()
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@property
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@abstractmethod
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def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
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"""
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Returns the current velocity of the action/control dimension.
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The dimensionality has to match the action/control dimension.
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This is not required when exclusively using position control,
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it should, however, be implemented regardless.
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E.g. The joint velocities that are directly or indirectly controlled by the action.
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"""
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raise NotImplementedError()
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def step(self, action: np.ndarray):
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""" This function generates a trajectory based on a MP and then does the usual loop over reset and step"""
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# TODO: Think about sequencing
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# TODO: Reward Function rather here?
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# agent to learn when to release the ball
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mp_params, env_spec_params = self._episode_callback(action)
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trajectory, velocity = self.get_trajectory(mp_params)
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trajectory_length = len(trajectory)
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if self.verbose >=2 :
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actions = np.zeros(shape=(trajectory_length,) + self.env.action_space.shape)
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observations = np.zeros(shape=(trajectory_length,) + self.env.observation_space.shape,
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dtype=self.env.observation_space.dtype)
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rewards = np.zeros(shape=(trajectory_length,))
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trajectory_return = 0
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infos = dict()
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for t, pos_vel in enumerate(zip(trajectory, velocity)):
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step_action = self.controller.get_action(pos_vel[0], pos_vel[1], self.current_pos, self.current_vel)
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step_action = self._step_callback(t, env_spec_params, step_action) # include possible callback info
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c_action = np.clip(step_action, self.env.action_space.low, self.env.action_space.high)
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obs, c_reward, done, info = self.env.step(c_action)
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if self.verbose >= 2:
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actions[t, :] = c_action
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rewards[t] = c_reward
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observations[t, :] = obs
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trajectory_return += c_reward
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for k, v in info.items():
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elems = infos.get(k, [None] * trajectory_length)
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elems[t] = v
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infos[k] = elems
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# infos['step_infos'].append(info)
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if self.render_mode:
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self.render(mode=self.render_mode, **self.render_kwargs)
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if done:
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break
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infos.update({k: v[:t + 1] for k, v in infos.items()})
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if self.verbose >= 2:
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infos['trajectory'] = trajectory
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infos['step_actions'] = actions[:t + 1]
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infos['step_observations'] = observations[:t + 1]
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infos['step_rewards'] = rewards[:t + 1]
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infos['trajectory_length'] = t + 1
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done = True
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return self.get_observation_from_step(obs), trajectory_return, done, infos
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def reset(self):
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return self.get_observation_from_step(self.env.reset())
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def render(self, mode='human', **kwargs):
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"""Only set render options here, such that they can be used during the rollout.
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This only needs to be called once"""
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self.render_mode = mode
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self.render_kwargs = kwargs
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# self.env.render(mode=self.render_mode, **self.render_kwargs)
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self.env.render(mode=self.render_mode)
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def get_observation_from_step(self, observation: np.ndarray) -> np.ndarray:
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return observation[self.active_obs]
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def plot_trajs(self, des_trajs, des_vels):
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import matplotlib.pyplot as plt
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import matplotlib
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matplotlib.use('TkAgg')
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pos_fig = plt.figure('positions')
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vel_fig = plt.figure('velocities')
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for i in range(des_trajs.shape[1]):
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plt.figure(pos_fig.number)
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plt.subplot(des_trajs.shape[1], 1, i + 1)
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plt.plot(np.ones(des_trajs.shape[0])*self.current_pos[i])
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plt.plot(des_trajs[:, i])
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plt.figure(vel_fig.number)
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plt.subplot(des_vels.shape[1], 1, i + 1)
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plt.plot(np.ones(des_trajs.shape[0])*self.current_vel[i])
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plt.plot(des_vels[:, i])
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@@ -0,0 +1,22 @@
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from mp_pytorch.mp.dmp import DMP
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from mp_pytorch.mp.promp import ProMP
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from mp_pytorch.mp.idmp import IDMP
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from mp_pytorch.basis_gn.basis_generator import BasisGenerator
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ALL_TYPES = ["promp", "dmp", "idmp"]
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def get_movement_primitive(
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movement_primitives_type: str, action_dim: int, basis_generator: BasisGenerator, **kwargs
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):
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movement_primitives_type = movement_primitives_type.lower()
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if movement_primitives_type == "promp":
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return ProMP(basis_generator, action_dim, **kwargs)
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elif movement_primitives_type == "dmp":
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return DMP(basis_generator, action_dim, **kwargs)
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elif movement_primitives_type == 'idmp':
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return IDMP(basis_generator, action_dim, **kwargs)
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else:
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raise ValueError(f"Specified movement primitive type {movement_primitives_type} not supported, "
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f"please choose one of {ALL_TYPES}.")
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@@ -0,0 +1,20 @@
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from mp_pytorch import LinearPhaseGenerator, ExpDecayPhaseGenerator
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from mp_pytorch.phase_gn.rhythmic_phase_generator import RhythmicPhaseGenerator
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from mp_pytorch.phase_gn.smooth_phase_generator import SmoothPhaseGenerator
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ALL_TYPES = ["linear", "exp", "rhythmic", "smooth"]
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def get_phase_generator(phase_generator_type, **kwargs):
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phase_generator_type = phase_generator_type.lower()
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if phase_generator_type == "linear":
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return LinearPhaseGenerator(**kwargs)
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elif phase_generator_type == "exp":
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return ExpDecayPhaseGenerator(**kwargs)
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elif phase_generator_type == "rhythmic":
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return RhythmicPhaseGenerator(**kwargs)
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elif phase_generator_type == "smooth":
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return SmoothPhaseGenerator(**kwargs)
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else:
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raise ValueError(f"Specified phase generator type {phase_generator_type} not supported, "
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f"please choose one of {ALL_TYPES}.")
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