diff --git a/examples/mujoco_panda_reach_sac.py b/examples/mujoco_panda_reach_sac.py new file mode 100644 index 00000000..268ba1aa --- /dev/null +++ b/examples/mujoco_panda_reach_sac.py @@ -0,0 +1,159 @@ +import numpy as np + +import torch +import torch.nn as nn +import torch.optim as optim +import torch.nn.functional as F + +from mushroom_rl.algorithms.actor_critic import SAC +from mushroom_rl.core import Core, Logger +from mushroom_rl.environments.mujoco_envs.reach import Reach + +from tqdm import trange + + +class CriticNetwork(nn.Module): + def __init__(self, input_shape, output_shape, n_features, **kwargs): + super().__init__() + + n_input = input_shape[-1] + n_output = output_shape[0] + + self._h1 = nn.Linear(n_input, n_features) + self._h2 = nn.Linear(n_features, n_features) + self._h3 = nn.Linear(n_features, n_output) + + nn.init.xavier_uniform_(self._h1.weight, gain=nn.init.calculate_gain("relu")) + nn.init.xavier_uniform_(self._h2.weight, gain=nn.init.calculate_gain("relu")) + nn.init.xavier_uniform_(self._h3.weight, gain=nn.init.calculate_gain("linear")) + + def forward(self, state, action): + state_action = torch.cat((state.float(), action.float()), dim=1) + features1 = F.relu(self._h1(state_action)) + features2 = F.relu(self._h2(features1)) + q = self._h3(features2) + + return torch.squeeze(q) + + +class ActorNetwork(nn.Module): + def __init__(self, input_shape, output_shape, n_features, **kwargs): + super(ActorNetwork, self).__init__() + + n_input = input_shape[-1] + n_output = output_shape[0] + + self._h1 = nn.Linear(n_input, n_features) + self._h2 = nn.Linear(n_features, n_features) + self._h3 = nn.Linear(n_features, n_output) + + nn.init.xavier_uniform_(self._h1.weight, gain=nn.init.calculate_gain("relu")) + nn.init.xavier_uniform_(self._h2.weight, gain=nn.init.calculate_gain("relu")) + nn.init.xavier_uniform_(self._h3.weight, gain=nn.init.calculate_gain("linear")) + + def forward(self, state): + features1 = F.relu(self._h1(torch.squeeze(state, 1).float())) + features2 = F.relu(self._h2(features1)) + a = self._h3(features2) + + return a + + +def experiment(alg, n_epochs, n_steps, n_episodes_test): + np.random.seed() + + logger = Logger(alg.__name__, results_dir=None) + logger.strong_line() + logger.info("Experiment Algorithm: " + alg.__name__) + + # MDP + mdp = Reach() + + # Settings + initial_replay_size = 5_000 + max_replay_size = 500_000 + batch_size = 256 + n_features = 256 + warmup_transitions = 10_000 + tau = 5e-3 + lr_alpha = 3e-4 + + # Approximator + actor_input_shape = mdp.info.observation_space.shape + actor_mu_params = dict( + network=ActorNetwork, + n_features=n_features, + input_shape=actor_input_shape, + output_shape=mdp.info.action_space.shape, + ) + actor_sigma_params = dict( + network=ActorNetwork, + n_features=n_features, + input_shape=actor_input_shape, + output_shape=mdp.info.action_space.shape, + ) + + actor_optimizer = {"class": optim.Adam, "params": {"lr": 1e-4}} + + critic_input_shape = (actor_input_shape[0] + mdp.info.action_space.shape[0],) + critic_params = dict( + network=CriticNetwork, + optimizer={"class": optim.Adam, "params": {"lr": 3e-4}}, + loss=F.mse_loss, + n_features=n_features, + input_shape=critic_input_shape, + output_shape=(1,), + ) + + # Agent + agent = alg( + mdp.info, + actor_mu_params, + actor_sigma_params, + actor_optimizer, + critic_params, + batch_size, + initial_replay_size, + max_replay_size, + warmup_transitions, + tau, + lr_alpha, + critic_fit_params=None, + ) + + # Algorithm + core = Core(agent, mdp) + + # RUN + dataset = core.evaluate(n_episodes=n_episodes_test, render=False) + + J = np.mean(dataset.discounted_return) + R = np.mean(dataset.undiscounted_return) + E = agent.policy.entropy(dataset.state) + + logger.epoch_info(0, J=J, R=R, entropy=E) + + core.learn( + n_steps=initial_replay_size, n_steps_per_fit=initial_replay_size, quiet=True + ) + + for n in trange(n_epochs, leave=False): + core.learn(n_steps=n_steps, n_steps_per_fit=1, quiet=True) + dataset = core.evaluate(n_episodes=n_episodes_test, render=False, quiet=True) + + J = np.mean(dataset.discounted_return) + R = np.mean(dataset.undiscounted_return) + E = agent.policy.entropy(dataset.state) + + logger.epoch_info(n + 1, J=J, R=R, entropy=E) + + logger.info("Press a button to visualize") + input() + core.evaluate(n_episodes=5, render=True) + + +if __name__ == "__main__": + algs = [SAC] + + for alg in algs: + experiment(alg=alg, n_epochs=50, n_steps=30000, n_episodes_test=10) diff --git a/mushroom_rl/environments/mujoco_envs/data/panda/assets/finger_0.obj b/mushroom_rl/environments/mujoco_envs/data/panda/assets/finger_0.obj new file mode 100644 index 00000000..104cf631 --- /dev/null +++ b/mushroom_rl/environments/mujoco_envs/data/panda/assets/finger_0.obj @@ -0,0 +1,2488 @@ +mtllib material_0.mtl +usemtl material_0 +v -0.00090000 0.00942300 0.04836700 +v -0.00090000 0.00942300 0.04836700 +v -0.00090000 0.00942300 0.04836700 +v -0.00090000 0.00942300 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5622//5622 +f 5610//5610 5662//5662 5638//5638 +f 3913//3913 3906//3906 3896//3896 +f 3913//3913 3896//3896 3920//3920 +f 3913//3913 3920//3920 3919//3919 diff --git a/mushroom_rl/environments/mujoco_envs/data/panda/panda.xml b/mushroom_rl/environments/mujoco_envs/data/panda/panda.xml new file mode 100644 index 00000000..15e6631a --- /dev/null +++ b/mushroom_rl/environments/mujoco_envs/data/panda/panda.xml @@ -0,0 +1,277 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/mushroom_rl/environments/mujoco_envs/data/panda/panda.yaml b/mushroom_rl/environments/mujoco_envs/data/panda/panda.yaml new file mode 100644 index 00000000..555e5251 --- /dev/null +++ b/mushroom_rl/environments/mujoco_envs/data/panda/panda.yaml @@ -0,0 +1,100 @@ +workspace: + limits: + x: [-0.95, 0.95] + y: [-0.95, 0.95] + z: [-0.05, 1.5] + min: + - -0.95 + - -0.95 + - -0.05 + max: + - 0.95 + - 0.95 + - 1.5 + +joint_pos: + limits: + joint1_pos: [-2.8973, 2.8973] + joint2_pos: [-1.7628, 1.7628] + joint3_pos: [-2.8973, 2.8973] + joint4_pos: [-3.0718, -0.0698] + joint5_pos: [-2.8973, 2.8973] + joint6_pos: [-0.0175, 3.7525] + joint7_pos: [-2.8973, 2.8973] + finger_joint1_pos: [0, 0.04] + finger_joint2_pos: [0, 0.04] + min: + - -2.8973 + - -1.7628 + - -2.8973 + - -3.0718 + - -2.8973 + - -0.0175 + - -2.8973 + - 0 + - 0 + max: + - 2.8973 + - 1.7628 + - 2.8973 + - -0.0698 + - 2.8973 + - 3.7525 + - 2.8973 + - 0.04 + - 0.04 +joint_vel: + limits: + joint1_vel: [-2.175, 2.1750] + joint2_vel: [-2.175, 2.1750] + joint3_vel: [-2.175, 2.1750] + joint4_vel: [-2.175, 2.1750] + joint5_vel: [-2.61, 2.6100] + joint6_vel: [-2.61, 2.6100] + joint7_vel: [-2.61, 2.6100] + min: + - 0 + - 0 + - 0 + - 0 + - 0 + - 0 + - 0 + max: + - 2.1750 + - 2.1750 + - 2.1750 + - 2.1750 + - 2.6100 + - 2.6100 + - 2.6100 + +torque: + limits: + actuator1: [-87.0, 87.0] + actuator2: [-87.0, 87.0] + actuator3: [-87.0, 87.0] + actuator4: [-87.0, 87.0] + actuator5: [-12.0, 12.0] + actuator6: [-12.0, 12.0] + actuator7: [-12.0, 12.0] + actuator8: [0.0, 255.0] + min: + - -87.0 + - -87.0 + - -87.0 + - -87.0 + - -12.0 + - -12.0 + - -12.0 + - 0.0 + max: + - 87.0 + - 87.0 + - 87.0 + - 87.0 + - 12.0 + - 12.0 + - 12.0 + - 255.0 + diff --git a/mushroom_rl/environments/mujoco_envs/data/panda/reach/reach.xml b/mushroom_rl/environments/mujoco_envs/data/panda/reach/reach.xml new file mode 100644 index 00000000..40879751 --- /dev/null +++ b/mushroom_rl/environments/mujoco_envs/data/panda/reach/reach.xml @@ -0,0 +1,37 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/mushroom_rl/environments/mujoco_envs/panda.py b/mushroom_rl/environments/mujoco_envs/panda.py new file mode 100644 index 00000000..330ebad1 --- /dev/null +++ b/mushroom_rl/environments/mujoco_envs/panda.py @@ -0,0 +1,169 @@ +import yaml +from pathlib import Path + +import mujoco +import numpy as np + +from mushroom_rl.environments.mujoco import MuJoCo, ObservationType + + +class Panda(MuJoCo): + + config_path = ( + Path(__file__).resolve().parent / "data" / "panda" / "panda.yaml" + ).as_posix() + + with open(config_path, "r") as file: + config = yaml.safe_load(file) + + # Access the loaded configuration + WORKSPACE_LIMITS = config["workspace"]["limits"] + WORKSPACE_MIN = np.array(config["workspace"]["min"]) + WORKSPACE_MAX = np.array(config["workspace"]["max"]) + + JOINT_POS_LIMITS = config["joint_pos"]["limits"] + JOINT_POS_MIN = np.array(config["joint_pos"]["min"]) + JOINT_POS_MAX = np.array(config["joint_pos"]["max"]) + + JOINT_VEL_LIMITS = config["joint_vel"]["limits"] + JOINT_VEL_MIN = np.array(config["joint_vel"]["min"]) + JOINT_VEL_MAX = np.array(config["joint_vel"]["max"]) + + TORQUE_LIMITS = config["torque"]["limits"] + TORQUE_MIN = np.array(config["torque"]["min"]) + TORQUE_MAX = np.array(config["torque"]["max"]) + + def __init__( + self, + xml_path, + gamma, + horizon, + n_substeps, + additional_data_spec=None, + collision_groups=None, + actuation_spec=None, + **viewer_params, + ): + actuation_spec = actuation_spec or [ + "actuator1", + "actuator2", + "actuator3", + "actuator4", + "actuator5", + "actuator6", + "actuator7", + "actuator8", + ] + + observation_spec = [ + ("joint1_pos", "joint1", ObservationType.JOINT_POS), + ("joint2_pos", "joint2", ObservationType.JOINT_POS), + ("joint3_pos", "joint3", ObservationType.JOINT_POS), + ("joint4_pos", "joint4", ObservationType.JOINT_POS), + ("joint5_pos", "joint5", ObservationType.JOINT_POS), + ("joint6_pos", "joint6", ObservationType.JOINT_POS), + ("joint7_pos", "joint7", ObservationType.JOINT_POS), + ("finger_joint1_pos", "finger_joint1", ObservationType.JOINT_POS), + ("finger_joint2_pos", "finger_joint2", ObservationType.JOINT_POS), + ("gripper_pos", "gripper", ObservationType.SITE_POS), + ("joint1_vel", "joint1", ObservationType.JOINT_VEL), + ("joint2_vel", "joint2", ObservationType.JOINT_VEL), + ("joint3_vel", "joint3", ObservationType.JOINT_VEL), + ("joint4_vel", "joint4", ObservationType.JOINT_VEL), + ("joint5_vel", "joint5", ObservationType.JOINT_VEL), + ("joint6_vel", "joint6", ObservationType.JOINT_VEL), + ("joint7_vel", "joint7", ObservationType.JOINT_VEL), + ] + + additional_data_spec = additional_data_spec or [] + + additional_data_spec += [ + ("joint1_pos", "joint1", ObservationType.JOINT_POS), + ("joint2_pos", "joint2", ObservationType.JOINT_POS), + ("joint3_pos", "joint3", ObservationType.JOINT_POS), + ("joint4_pos", "joint4", ObservationType.JOINT_POS), + ("joint5_pos", "joint5", ObservationType.JOINT_POS), + ("joint6_pos", "joint6", ObservationType.JOINT_POS), + ("joint7_pos", "joint7", ObservationType.JOINT_POS), + ("finger_joint1_pos", "finger_joint1", ObservationType.JOINT_POS), + ("finger_joint2_pos", "finger_joint2", ObservationType.JOINT_POS), + ("gripper_pos", "gripper", ObservationType.SITE_POS), + ("hand_rot", "hand", ObservationType.BODY_ROT), + ("left_fingertip_pos", "left_fingertip", ObservationType.SITE_POS), + ("right_fingertip_pos", "right_fingertip", ObservationType.SITE_POS), + ("left_cube_pos", "left_cube", ObservationType.SITE_POS), + ("right_cube_pos", "right_cube", ObservationType.SITE_POS), + ] + + collision_groups = collision_groups or [] + + collision_groups += [ + ("hand", ["hand_c"]), + ( + "left_finger", + [ + "left_fingertip_pad_collision_1", + "left_fingertip_pad_collision_2", + "left_fingertip_pad_collision_3", + "left_fingertip_pad_collision_4", + "left_fingertip_pad_collision_5", + ], + ), + ( + "right_finger", + [ + "right_fingertip_pad_collision_1", + "right_fingertip_pad_collision_2", + "right_fingertip_pad_collision_3", + "right_fingertip_pad_collision_4", + "right_fingertip_pad_collision_5", + ], + ), + ( + "robot", + [ + "link0_c", + "link1_c", + "link2_c", + "link3_c", + "link4_c", + "link5_c0", + "link5_c1", + "link5_c2", + "link6_c", + "link7_c", + ], + ), + ("floor", ["floor"]), + ] + + super().__init__( + xml_path, + gamma=gamma, + horizon=horizon, + actuation_spec=actuation_spec, + observation_spec=observation_spec, + additional_data_spec=additional_data_spec, + collision_groups=collision_groups, + n_substeps=n_substeps, + **viewer_params, + ) + + def _load_keyframe(self, name: str): + keyframe = self._model.keyframe(name) + mujoco.mj_resetDataKeyframe(self._model, self._data, keyframe.id) # type: ignore + + def setup(self, obs): + super().setup(obs) + self._load_keyframe("home") + + def _compute_action(self, obs, action): + action = super()._compute_action(obs, action) + self._data.qfrc_applied[:7] = self._data.qfrc_bias[:7] + return action + + def _get_random_workspace_pos(self, x=None, y=None, z=None): + x_pos = x or np.random.uniform(*self.WORKSPACE_LIMITS["x"]) + y_pos = y or np.random.uniform(*self.WORKSPACE_LIMITS["y"]) + z_pos = z or np.random.uniform(*self.WORKSPACE_LIMITS["z"]) + return np.array([x_pos, y_pos, z_pos]) diff --git a/mushroom_rl/environments/mujoco_envs/reach.py b/mushroom_rl/environments/mujoco_envs/reach.py new file mode 100644 index 00000000..0c39e980 --- /dev/null +++ b/mushroom_rl/environments/mujoco_envs/reach.py @@ -0,0 +1,144 @@ +from pathlib import Path +import random + +import numpy as np +import mujoco + +from mushroom_rl.environments.mujoco import ObservationType +from mushroom_rl.rl_utils.spaces import Box +from mushroom_rl.environments.mujoco_envs.panda import Panda + + +class Reach(Panda): + + def __init__( + self, + gamma: float = 0.99, + horizon: int = 200, + gripper_goal_distance_reward_weight: float = 1.0, + ctrl_cost_weight: float = 1e-4, + normalize_reward: bool = False, + n_substeps: int = 10, + goal_noise_scale: float = 0.2, + **viewer_params, + ): + + xml_path = ( + Path(__file__).resolve().parent / "data" / "panda" / "reach" / "reach.xml" + ).as_posix() + + additional_data_spec = [ + ("goal_pos", "goal", ObservationType.BODY_POS), + ] + + self._gripper_goal_distance = 0 + + self._gripper_goal_distance_reward_weight = gripper_goal_distance_reward_weight + self._ctrl_cost_weight = ctrl_cost_weight + + self._gripper_goal_distance_reward = 0 + self._ctrl_cost = 0 + + self._normalize_reward = normalize_reward + self._goal_noise_scale = goal_noise_scale + + if viewer_params: + viewer_params["camera_params"]["static"]["elevation"] = -30 + viewer_params["camera_params"]["static"]["distance"] = 4 + + super().__init__( + xml_path, + gamma=gamma, + horizon=horizon, + additional_data_spec=additional_data_spec, + n_substeps=n_substeps, + **viewer_params, + ) + + def _modify_mdp_info(self, mdp_info): + self.obs_helper.add_obs("goal_pos", 3) + mdp_info = super()._modify_mdp_info(mdp_info) + mdp_info.observation_space = Box(*self.obs_helper.get_obs_limits()) + return mdp_info + + def _create_observation(self, obs): + obs = super()._create_observation(obs) + goal_pos = self._read_data("goal_pos") + obs = np.concatenate([obs, goal_pos]) + return obs + + def _get_goal_pos(self, obs): + return self.obs_helper.get_from_obs(obs, "goal_pos") + + def _get_gripper_pos(self, obs): + return self.obs_helper.get_from_obs(obs, "gripper_pos") + + def _get_gripper_goal_distance(self, obs): + gripper_pos = self._get_gripper_pos(obs) + goal_pos = self._get_goal_pos(obs) + return np.linalg.norm(goal_pos - gripper_pos) + + def _get_gripper_goal_distance_reward(self, obs): + distance_reward = self._get_gripper_goal_distance(obs) + return self._gripper_goal_distance_reward_weight * distance_reward + + def _get_ctrl_cost(self, action): + # [:-1] to exclude the actions of the fingers + ctrl_cost = np.sum(np.square(action[:-1])) + return self._ctrl_cost_weight * ctrl_cost + + def reward(self, obs, action, next_obs, absorbing): + if absorbing: + return 100 + gripper_goal_distance_reward = self._get_gripper_goal_distance_reward(next_obs) + ctrl_cost = self._get_ctrl_cost(action) + reward = -gripper_goal_distance_reward - ctrl_cost + return reward + + def is_absorbing(self, obs): + return self._get_gripper_goal_distance(obs) < 0.1 + + def setup(self, obs): + super().setup(obs) + self._randomize_goal_position() + mujoco.mj_forward(self._model, self._data) # type: ignore + + def _create_info_dictionary(self, obs): + info = super()._create_info_dictionary(obs) + info["gripper_goal_distance_reward"] = self._get_gripper_goal_distance(obs) + # info["ctrl_cost"] = self.ctrl_cost(action) + return info + + def _get_error(self, obs): + gripper_pos = self.obs_helper.get_from_obs(obs, "gripper_pos") + goal_pos = self.obs_helper.get_from_obs(obs, "goal_pos") + error = np.linalg.norm(gripper_pos - goal_pos).item() + return error + + def _randomize_goal_position(self): + random_workspace_pos = self.sample_workspace() + self._data.mocap_pos[0][:] = random_workspace_pos + + def sample_workspace(self): + # Workspace dimensions + radius = 0.8 # mm + workspace_height = 1 # mm + + import math + + while True: + # Generate random angle and radius + angle = random.uniform(-np.pi / 2, np.pi / 2) + r = random.uniform(0.3, radius) + + # Convert polar coordinates to Cartesian + + x = r * math.cos(angle) + y = r * math.sin(angle) + + # Generate random height within the workspace + z = random.uniform(0, workspace_height) + + # Check if the point is within the workspace (excluding the cutout) + if x >= -855 or y >= 0: + return x, y, z