From 72b6438d3ada157302abe91da256a31192204b54 Mon Sep 17 00:00:00 2001 From: jogima-cyber Date: Tue, 1 Oct 2024 19:13:03 -0400 Subject: [PATCH] SoloParkour RL controller example is added. --- .../checkpoints/SoloParkour/go2_flat.pt | Bin 0 -> 1517043 bytes Go2Py/control/cat.py | 300 ++++++++++++++++++ examples/06-CaT-RL-controller.ipynb | 294 +++++++++++++++++ 3 files changed, 594 insertions(+) create mode 100644 Go2Py/assets/checkpoints/SoloParkour/go2_flat.pt create mode 100755 Go2Py/control/cat.py create mode 100644 examples/06-CaT-RL-controller.ipynb diff --git a/Go2Py/assets/checkpoints/SoloParkour/go2_flat.pt b/Go2Py/assets/checkpoints/SoloParkour/go2_flat.pt new file mode 100644 index 0000000000000000000000000000000000000000..6efbbac044f5d3a892470c9fbf66dc0a1e3bf7e9 GIT binary patch literal 1517043 zcmb5V2{=_>`~PoF2pNhp79t`UbJo2LiHcO3G^%6_*=SM}Wge0WDME@;nL?a(Z%Ro? zsYsJ1jY?@AH2hDWKF{~t=Xsvr|9Ab)bsgt&uYF$Yz1Df}z3#o%+B025#e{?;C58Uu 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a/Go2Py/control/cat.py b/Go2Py/control/cat.py new file mode 100755 index 0000000..74c7bad --- /dev/null +++ b/Go2Py/control/cat.py @@ -0,0 +1,300 @@ +import os +import time + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +class RunningMeanStd(nn.Module): + def __init__(self, shape = (), epsilon=1e-08): + super(RunningMeanStd, self).__init__() + self.register_buffer("running_mean", torch.zeros(shape)) + self.register_buffer("running_var", torch.ones(shape)) + self.register_buffer("count", torch.ones(())) + + self.epsilon = epsilon + + def forward(self, obs, update = True): + if update: + self.update(obs) + + return (obs - self.running_mean) / torch.sqrt(self.running_var + self.epsilon) + + def update(self, x): + """Updates the mean, var and count from a batch of samples.""" + batch_mean = torch.mean(x, dim=0) + batch_var = torch.var(x, correction=0, dim=0) + batch_count = x.shape[0] + self.update_from_moments(batch_mean, batch_var, batch_count) + + def update_from_moments(self, batch_mean, batch_var, batch_count): + """Updates from batch mean, variance and count moments.""" + self.running_mean, self.running_var, self.count = update_mean_var_count_from_moments( + self.running_mean, self.running_var, self.count, batch_mean, batch_var, batch_count + ) + +def update_mean_var_count_from_moments( + mean, var, count, batch_mean, batch_var, batch_count +): + """Updates the mean, var and count using the previous mean, var, count and batch values.""" + delta = batch_mean - mean + tot_count = count + batch_count + + new_mean = mean + delta * batch_count / tot_count + m_a = var * count + m_b = batch_var * batch_count + M2 = m_a + m_b + torch.square(delta) * count * batch_count / tot_count + new_var = M2 / tot_count + new_count = tot_count + + return new_mean, new_var, new_count + +def layer_init(layer, std=np.sqrt(2), bias_const=0.0): + torch.nn.init.orthogonal_(layer.weight, std) + torch.nn.init.constant_(layer.bias, bias_const) + return layer + +class Agent(nn.Module): + def __init__(self): + super().__init__() + self.critic = nn.Sequential( + layer_init(nn.Linear(45, 512)), + nn.ELU(), + layer_init(nn.Linear(512, 256)), + nn.ELU(), + layer_init(nn.Linear(256, 128)), + nn.ELU(), + layer_init(nn.Linear(128, 1), std=1.0), + ) + self.actor_mean = nn.Sequential( + layer_init(nn.Linear(45, 512)), + nn.ELU(), + layer_init(nn.Linear(512, 256)), + nn.ELU(), + layer_init(nn.Linear(256, 128)), + nn.ELU(), + layer_init(nn.Linear(128, 12), std=0.01), + ) + self.actor_logstd = nn.Parameter(torch.zeros(1, 12)) + + self.obs_rms = RunningMeanStd(shape = (45,)) + self.value_rms = RunningMeanStd(shape = ()) + + def get_value(self, x): + return self.critic(x) + + def get_action(self, x): + action_mean = self.actor_mean(x) + return action_mean + +class Policy: + def __init__(self, checkpoint_path): + self.agent = Agent() + actor_sd = torch.load(checkpoint_path) + self.agent.load_state_dict(actor_sd) + + def __call__(self, obs, info): + with torch.no_grad(): + action = self.agent.get_action(self.agent.obs_rms(obs.unsqueeze(0), update = False)) + return action + +class CommandInterface: + def __init__(self, limits=None): + self.limits = limits + self.x_vel_cmd, self.y_vel_cmd, self.yaw_vel_cmd = 0.0, 0.0, 0.0 + + def get_command(self): + command = np.zeros((3,)) + command[0] = self.x_vel_cmd + command[1] = self.y_vel_cmd + command[2] = self.yaw_vel_cmd + return command, False + + +class CaTAgent: + def __init__(self, command_profile, robot): + self.robot = robot + self.command_profile = command_profile + # self.lcm_bridge = LCMBridgeClient(robot_name=self.robot_name) + self.sim_dt = 0.001 + self.decimation = 20 + self.dt = self.sim_dt * self.decimation + self.timestep = 0 + + self.device = "cpu" + + joint_names = [ + "FL_hip_joint", + "FL_thigh_joint", + "FL_calf_joint", + "FR_hip_joint", + "FR_thigh_joint", + "FR_calf_joint", + "RL_hip_joint", + "RL_thigh_joint", + "RL_calf_joint", + "RR_hip_joint", + "RR_thigh_joint", + "RR_calf_joint", + ] + + policy_joint_names = joint_names + + unitree_joint_names = [ + "FR_hip_joint", + "FR_thigh_joint", + "FR_calf_joint", + "FL_hip_joint", + "FL_thigh_joint", + "FL_calf_joint", + "RR_hip_joint", + "RR_thigh_joint", + "RR_calf_joint", + "RL_hip_joint", + "RL_thigh_joint", + "RL_calf_joint", + ] + + policy_to_unitree_map = [] + unitree_to_policy_map = [] + for i, policy_joint_name in enumerate(policy_joint_names): + id = np.where([name == policy_joint_name for name in unitree_joint_names])[0][0] + policy_to_unitree_map.append((i, id)) + self.policy_to_unitree_map = np.array(policy_to_unitree_map).astype(np.uint32) + + for i, unitree_joint_name in enumerate(unitree_joint_names): + id = np.where([name == unitree_joint_name for name in policy_joint_names])[0][0] + unitree_to_policy_map.append((i, id)) + self.unitree_to_policy_map = np.array(unitree_to_policy_map).astype(np.uint32) + + default_joint_angles = { + "FL_hip_joint": 0.1, + "RL_hip_joint": 0.1, + "FR_hip_joint": -0.1, + "RR_hip_joint": -0.1, + "FL_thigh_joint": 0.8, + "RL_thigh_joint": 1.0, + "FR_thigh_joint": 0.8, + "RR_thigh_joint": 1.0, + "FL_calf_joint": -1.5, + "RL_calf_joint": -1.5, + "FR_calf_joint": -1.5, + "RR_calf_joint": -1.5 + } + self.default_dof_pos = np.array( + [ + default_joint_angles[name] + for name in joint_names + ] + ) + + self.default_dof_pos = self.default_dof_pos + + self.p_gains = np.zeros(12) + self.d_gains = np.zeros(12) + for i in range(12): + self.p_gains[i] = 20.0 + self.d_gains[i] = 0.5 + + print(f"p_gains: {self.p_gains}") + + self.commands = np.zeros(3) + self.actions = torch.zeros((1, 12)) + self.last_actions = torch.zeros(12) + self.gravity_vector = np.zeros(3) + self.dof_pos = np.zeros(12) + self.dof_vel = np.zeros(12) + self.body_linear_vel = np.zeros(3) + self.body_angular_vel = np.zeros(3) + self.joint_pos_target = np.zeros(12) + self.joint_vel_target = np.zeros(12) + self.torques = np.zeros(12) + self.contact_state = np.ones(4) + self.test = 0 + + def wait_for_state(self): + # return self.lcm_bridge.getStates(timeout=2) + pass + + def get_obs(self): + cmds, reset_timer = self.command_profile.get_command() + self.commands[:] = cmds + + # self.state = self.wait_for_state() + joint_state = self.robot.getJointStates() + if joint_state is not None: + self.gravity_vector = self.robot.getGravityInBody() + self.dof_pos = np.array(joint_state['q'])[self.unitree_to_policy_map[:, 1]] + self.dof_vel = np.array(joint_state['dq'])[self.unitree_to_policy_map[:, 1]] + self.body_angular_vel = self.robot.getIMU()["gyro"] + + ob = np.concatenate( + ( + self.body_angular_vel * 0.25, + self.commands * np.array([2.0, 2.0, 0.25]), + self.gravity_vector[:, 0], + self.dof_pos * 1.0, + self.dof_vel * 0.05, + self.actions[0] + ), + axis=0, + ) + + return torch.tensor(ob, device=self.device).float() + + def publish_action(self, action, hard_reset=False): + # command_for_robot = UnitreeLowCommand() + self.joint_pos_target = ( + action[0, :12].detach().cpu().numpy() * 0.25 + ).flatten() + self.joint_pos_target += self.default_dof_pos + self.joint_vel_target = np.zeros(12) + # command_for_robot.q_des = self.joint_pos_target + # command_for_robot.dq_des = self.joint_vel_target + # command_for_robot.kp = self.p_gains + # command_for_robot.kd = self.d_gains + # command_for_robot.tau_ff = np.zeros(12) + if hard_reset: + command_for_robot.id = -1 + + self.torques = (self.joint_pos_target - self.dof_pos) * self.p_gains + ( + self.joint_vel_target - self.dof_vel + ) * self.d_gains + # self.lcm_bridge.sendCommands(command_for_robot) + self.robot.setCommands(self.joint_pos_target[self.policy_to_unitree_map[:, 1]], + self.joint_vel_target[self.policy_to_unitree_map[:, 1]], + self.p_gains[self.policy_to_unitree_map[:, 1]], + self.d_gains[self.policy_to_unitree_map[:, 1]], + np.zeros(12)) + + def reset(self): + self.actions = torch.zeros((1, 12)) + self.time = time.time() + self.timestep = 0 + return self.get_obs() + + def step(self, actions, hard_reset=False): + self.last_actions = self.actions[:] + self.actions = actions + self.publish_action(self.actions, hard_reset=hard_reset) + # time.sleep(max(self.dt - (time.time() - self.time), 0)) + # if self.timestep % 100 == 0: + # print(f"frq: {1 / (time.time() - self.time)} Hz") + self.time = time.time() + obs = self.get_obs() + + infos = { + "joint_pos": self.dof_pos[np.newaxis, :], + "joint_vel": self.dof_vel[np.newaxis, :], + "joint_pos_target": self.joint_pos_target[np.newaxis, :], + "joint_vel_target": self.joint_vel_target[np.newaxis, :], + "body_linear_vel": self.body_linear_vel[np.newaxis, :], + "body_angular_vel": self.body_angular_vel[np.newaxis, :], + "contact_state": self.contact_state[np.newaxis, :], + "body_linear_vel_cmd": self.commands[np.newaxis, 0:2], + "body_angular_vel_cmd": self.commands[np.newaxis, 2:], + } + + self.timestep += 1 + return obs, None, None, infos diff --git a/examples/06-CaT-RL-controller.ipynb b/examples/06-CaT-RL-controller.ipynb new file mode 100644 index 0000000..5ab6cb7 --- /dev/null +++ b/examples/06-CaT-RL-controller.ipynb @@ -0,0 +1,294 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "RL policy based on the [SoloParkour: Constrained Reinforcement Learning for Visual Locomotion from Privileged Experience](https://arxiv.org/abs/2409.13678). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Flat Ground" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test In Simulation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from Go2Py.robot.fsm import FSM\n", + "from Go2Py.robot.remote import KeyboardRemote\n", + "from Go2Py.robot.safety import SafetyHypervisor\n", + "from Go2Py.sim.mujoco import Go2Sim\n", + "from Go2Py.control.cat import *\n", + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "robot = Go2Sim()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "remote = KeyboardRemote()\n", + "robot.sitDownReset()\n", + "safety_hypervisor = SafetyHypervisor(robot)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class CaTController:\n", + " def __init__(self, robot, remote, checkpoint):\n", + " self.remote = remote\n", + " self.robot = robot\n", + " self.policy = Policy(checkpoint)\n", + " self.command_profile = CommandInterface()\n", + " self.agent = CaTAgent(self.command_profile, self.robot)\n", + " self.init()\n", + "\n", + " def init(self):\n", + " self.obs = self.agent.reset()\n", + " self.policy_info = {}\n", + " self.command_profile.yaw_vel_cmd = 0.0\n", + " self.command_profile.x_vel_cmd = 0.0\n", + " self.command_profile.y_vel_cmd = 0.0\n", + "\n", + " def update(self, robot, remote):\n", + " action = self.policy(self.obs, self.policy_info)\n", + " self.obs, self.ret, self.done, self.info = self.agent.step(action)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/Go2py/Go2Py/control/cat.py:94: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " actor_sd = torch.load(checkpoint_path)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "p_gains: [20. 20. 20. 20. 20. 20. 20. 20. 20. 20. 20. 20.]\n" + ] + } + ], + "source": [ + "from Go2Py import ASSETS_PATH \n", + "import os\n", + "checkpoint_path = os.path.join(ASSETS_PATH, 'checkpoints/SoloParkour/go2_flat.pt')\n", + "controller = CaTController(robot, remote, checkpoint_path)\n", + "fsm = FSM(robot, remote, safety_hypervisor, user_controller_callback=controller.update)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "controller.command_profile.x_vel_cmd=-0.3\n", + "controller.command_profile.y_vel_cmd=0.3\n", + "controller.command_profile.yaw_vel_cmd = 0.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pressing `u` on the keyboard will make the robot stand up. This is equivalent to the `L2+A` combo of the Go2 builtin state machine. After the the robot is on its feet, pressing `s` will hand over the control the RL policy. This action is equivalent to the `start` key of the builtin controller. When you want to stop, pressing `u` again will act similarly to the real robot and locks it in standing mode. Finally, pressing `u` again will command the robot to sit down." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "fsm.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test on Real Robot (ToDo)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from Go2Py.robot.fsm import FSM\n", + "from Go2Py.robot.remote import UnitreeRemote\n", + "from Go2Py.robot.safety import SafetyHypervisor\n", + "from Go2Py.control.walk_these_ways import *" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from Go2Py.robot.interface import GO2Real\n", + "import numpy as np\n", + "robot = GO2Real(mode='lowlevel')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "remote = UnitreeRemote(robot)\n", + "safety_hypervisor = SafetyHypervisor(robot)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class walkTheseWaysController:\n", + " def __init__(self, robot, remote, checkpoint):\n", + " self.remote = remote\n", + " self.robot = robot\n", + " self.cfg = loadParameters(checkpoint)\n", + " self.policy = Policy(checkpoint)\n", + " self.command_profile = CommandInterface()\n", + " self.agent = WalkTheseWaysAgent(self.cfg, self.command_profile, self.robot)\n", + " self.agent = HistoryWrapper(self.agent)\n", + " self.init()\n", + "\n", + " def init(self):\n", + " self.obs = self.agent.reset()\n", + " self.policy_info = {}\n", + " self.command_profile.yaw_vel_cmd = 0.0\n", + " self.command_profile.x_vel_cmd = 0.0\n", + " self.command_profile.y_vel_cmd = 0.0\n", + " self.command_profile.stance_width_cmd=0.25\n", + " self.command_profile.footswing_height_cmd=0.08\n", + " self.command_profile.step_frequency_cmd = 3.0\n", + " self.command_profile.bodyHeight = 0.00\n", + "\n", + " def update(self, robot, remote):\n", + " action = self.policy(self.obs, self.policy_info)\n", + " self.obs, self.ret, self.done, self.info = self.agent.step(action)\n", + " vy = -robot.getRemoteState().lx\n", + " vx = robot.getRemoteState().ly\n", + " omega = -robot.getRemoteState().rx*2.2\n", + " self.command_profile.x_vel_cmd = vx*1.5\n", + " self.command_profile.y_vel_cmd = vy*1.5\n", + " self.command_profile.yaw_vel_cmd = omega" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "checkpoint = \"../Go2Py/assets/checkpoints/walk_these_ways/\"\n", + "controller = walkTheseWaysController(robot, remote, checkpoint)\n", + "safety_hypervisor = SafetyHypervisor(robot)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "controller.command_profile.pitch_cmd=0.0\n", + "controller.command_profile.body_height_cmd=0.0\n", + "controller.command_profile.footswing_height_cmd=0.04\n", + "controller.command_profile.roll_cmd=0.0\n", + "controller.command_profile.stance_width_cmd=0.2\n", + "controller.command_profile.x_vel_cmd=-0.2\n", + "controller.command_profile.y_vel_cmd=0.01\n", + "controller.command_profile.setGaitType(\"trotting\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fsm = FSM(robot, remote, safety_hypervisor, user_controller_callback=controller.update)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pressing `L2+A` to command the robot to stand up. After the the robot is on its feet, pressing `start` will hand over the control the RL policy. When you want to stop, pressing `L2+A` again will act similarly to the factory controller and locks the robot in standing mode. Finally, pressing `L2+A` again will command the robot to sit down." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fsm.close()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "b1-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}

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