453 lines
19 KiB
Python
453 lines
19 KiB
Python
"""
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FSM State Implementations
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Concrete implementations of different FSM states
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"""
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import numpy as np
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import onnxruntime as ort
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from FSM.fsm_base import FSMState, FSMStateName
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from common.joystick import ControlFlag
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from common.robot_data import RobotData
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from common.BasicFunction import clip_vector, gait_phase
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import os
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import yaml
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from scipy.spatial.transform import Rotation
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class FSMStateWALKAMP(FSMState):
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"""WALKAMP策略状态实现"""
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def _reset_internal_state(self):
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"""把所有随时间变化的内部状态重置成初始值"""
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# 1) 清空 obs / hist / actions
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self.observations_.fill(0.0)
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self.proprio_hist_buf_.fill(0.0)
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self.last_actions_.fill(0.0)
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self.actions_.fill(0.0)
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# 2) 标志位重置
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self.is_first_obs_ = True
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self.is_first_action_ = True
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self.is_first_step_ = True
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# 3) 期望关节 / 期望速度 / 力矩重置为“初始姿态”
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# 你已经有 self.joint_pos_array（mj 顺序，长度 len(self.joint_xml)）
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base = self.robot_data_.q_d_.shape[0] - self.motor_num_
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# 期望角 = 初始角
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self.robot_data_.q_d_[base:base + len(self.joint_xml)] = self.joint_pos_array
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# 期望速度 = 0
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self.robot_data_.q_dot_d_[base:base + len(self.joint_xml)] = 0.0
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# 期望力矩 = 0（位置控制）
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self.robot_data_.tau_d_[base:base + len(self.joint_xml)] = 0.0
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def __init__(self,
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robot_data: RobotData,
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config_path: str | None = None,
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base_dir: str | None = None,
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log_prefix: str = "FSMStateWALKAMP"):
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super().__init__(robot_data)
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self.log_prefix = log_prefix
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# 获取包路径
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current_dir = os.path.dirname(os.path.abspath(__file__))
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if config_path is None:
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config_path = os.path.join(current_dir, "config", "walk_amp.yaml")
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config_path = os.path.abspath(config_path)
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if base_dir is None:
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base_dir = os.path.dirname(os.path.dirname(config_path))
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with open(config_path, 'r') as f:
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policy_config = yaml.safe_load(f)
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# Load configuration exactly like C++
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self.action_num_ = policy_config.get('actions_size')
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self.motor_num_ = policy_config.get('motor_num')
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self.dt_ = policy_config.get('dt')
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# Size configuration
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size_config = policy_config.get('size', {})
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self.num_hist_ = size_config.get('num_hist')
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self.obs_size_ = size_config.get('observations_size')
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# Control configuration
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control_config = policy_config.get('control', {})
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self.action_scale_ = control_config.get('action_scale')
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# self.gait_cycle_period_ = control_config.get('gait_cycle_period', 1.0)
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self.decimation_ = control_config.get('decimation')
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self.warm_start_time_ = control_config.get(
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'warm_start_time',
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policy_config.get('warm_start_time', 0.3),
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)
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# Normalization configuration
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norm_config = policy_config.get('normalization', {})
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clip_config = norm_config.get('clip_scales', {})
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obs_config = norm_config.get('obs_scales', {})
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self.clip_obs_ = clip_config.get('clip_observations', 100.0)
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self.clip_act_ = clip_config.get('clip_actions', 100.0)
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self.lin_vel_scale_ = obs_config.get('lin_vel')
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self.ang_vel_scale_ = obs_config.get('ang_vel')
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self.dof_pos_scale_ = obs_config.get('dof_pos')
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self.dof_vel_scale_ = obs_config.get('dof_vel')
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# Initialize buffers and actions
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self.observations_ = np.zeros(self.obs_size_ * self.num_hist_, dtype=np.float32)
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self.proprio_hist_buf_ = np.zeros(self.obs_size_ * self.num_hist_, dtype=np.float32)
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self.last_actions_ = np.zeros(self.action_num_, dtype=np.float32)
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self.actions_ = np.zeros(self.action_num_, dtype=np.float32)
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self._warm_start_pose = np.zeros(self.motor_num_, dtype=np.float32)
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# Flags matching C++
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self.is_first_obs_ = True
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self.is_first_action_ = True
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# self.phase_locked = False
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self.timer_gait_ = 0.0
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gait_config = policy_config.get('gait', {})
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self.gait_cycle = gait_config.get('gait_cycle', 0.85)
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self.left_phase_ratio = gait_config.get('gait_air_ratio_l', 0.38)
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self.right_phase_ratio = gait_config.get('gait_air_ratio_r', 0.38)
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self.left_theta_offset = gait_config.get('gait_phase_offset_l', 0.38)
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self.right_theta_offset = gait_config.get('gait_phase_offset_r', 0.88)
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self.is_first_step_ = True
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step = (self.decimation_ if self.decimation_ else 1) * self.dt_
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if self.warm_start_time_ > 0 and step > 0:
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self._warm_start_steps = max(1, int(self.warm_start_time_ / step))
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else:
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self._warm_start_steps = 0
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self._warmup_inference_counter = 0
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# Initialize ONNX session
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self.model_path = os.path.join(base_dir, "model", policy_config["model_path"])
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self._init_onnx_session()
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self.joint_seq = None
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self.joint_pos_array_seq = None
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self.action_scale = None
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self.stiffness_array_seq = None
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self.damping_array_seq = None
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joint_names = policy_config.get('joint_names')
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if joint_names is None:
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raise ValueError("[FSMStateWALKAMP] Missing 'joint_names' in walk_amp.yaml")
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self.joint_seq = list(joint_names)
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if self.action_scale_ is None:
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raise ValueError("[FSMStateWALKAMP] Missing 'control.action_scale' in walk_amp.yaml")
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if np.isscalar(self.action_scale_):
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self.action_scale = np.full(len(self.joint_seq), float(self.action_scale_), dtype=np.float32)
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else:
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self.action_scale = np.array(self.action_scale_, dtype=np.float32)
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if len(self.action_scale) != len(self.joint_seq):
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raise ValueError(
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f"[FSMStateWALKAMP] control.action_scale length {len(self.action_scale)} does not match joint count {len(self.joint_seq)}"
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)
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init_state_config = policy_config.get('init_state', {})
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default_joint_angles = init_state_config.get('default_joint_angles')
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if default_joint_angles is None:
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raise ValueError("[FSMStateWALKAMP] Missing 'init_state.default_joint_angles' in walk_amp.yaml")
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self.joint_pos_array_seq = np.array(default_joint_angles, dtype=np.float32)
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if len(self.joint_pos_array_seq) != len(self.joint_seq):
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raise ValueError(
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f"[FSMStateWALKAMP] init_state.default_joint_angles length {len(self.joint_pos_array_seq)} does not match joint count {len(self.joint_seq)}"
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)
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gains_config = policy_config.get('gains', {})
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kp_values = gains_config.get('kp')
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kd_values = gains_config.get('kd')
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if kp_values is None or kd_values is None:
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raise ValueError("[FSMStateWALKAMP] Missing 'gains.kp' or 'gains.kd' in walk_amp.yaml")
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self.stiffness_array_seq = np.array(kp_values, dtype=np.float32)
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self.damping_array_seq = np.array(kd_values, dtype=np.float32)
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if len(self.stiffness_array_seq) != len(self.joint_seq):
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raise ValueError(
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f"[FSMStateWALKAMP] gains.kp length {len(self.stiffness_array_seq)} does not match joint count {len(self.joint_seq)}"
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)
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if len(self.damping_array_seq) != len(self.joint_seq):
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raise ValueError(
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f"[FSMStateWALKAMP] gains.kd length {len(self.damping_array_seq)} does not match joint count {len(self.joint_seq)}"
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)
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# # 设置从序列到实验室顺序的映射
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self.joint_xml = [
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"hip_pitch_l_joint", "hip_roll_l_joint", "hip_yaw_l_joint",
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"knee_pitch_l_joint", "ankle_pitch_l_joint", "ankle_roll_l_joint",
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"hip_pitch_r_joint", "hip_roll_r_joint", "hip_yaw_r_joint",
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"knee_pitch_r_joint", "ankle_pitch_r_joint", "ankle_roll_r_joint",
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"waist_yaw_joint", "waist_roll_joint", "waist_pitch_joint",
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"shoulder_pitch_l_joint", "shoulder_roll_l_joint", "shoulder_yaw_l_joint",
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"elbow_pitch_l_joint", "elbow_yaw_l_joint", "wrist_pitch_l_joint", "wrist_roll_l_joint",
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"shoulder_pitch_r_joint", "shoulder_roll_r_joint", "shoulder_yaw_r_joint",
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"elbow_pitch_r_joint", "elbow_yaw_r_joint", "wrist_pitch_r_joint", "wrist_roll_r_joint",
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]
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# 从MjXUML顺序映射到实验室顺序
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# self.mj2lab = np.array([self.joint_xml.index(joint) for joint in self.joint_seq])
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self.lab2mj = []
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for name in self.joint_seq:
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if name not in self.joint_xml:
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raise ValueError(f"[FSMStateWALKAMP] joint '{name}' from walk_amp.yaml not found in joint_xml!")
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self.lab2mj.append(self.joint_xml.index(name))
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self.lab2mj = np.array(self.lab2mj, dtype=int)
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# 从实验室顺序映射到MjXUML顺序
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# ====== 把 23 个 lab 关节 scatter 到 29 个 xml 里，多的 6 个保持默认 ======
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n_mj = len(self.joint_xml)
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# 29 长度，mujoco XML 顺序，先全 0 或者你想要的默认值
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self.joint_pos_array = np.zeros(n_mj, dtype=np.float32)
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self.stiffness_array = np.zeros(n_mj, dtype=np.float32)
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self.damping_array = np.zeros(n_mj, dtype=np.float32)
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# joint_pos_array_seq / stiffness_array_seq / damping_array_seq 是 23 长度，lab 顺序
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for lab_idx, mj_idx in enumerate(self.lab2mj):
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self.joint_pos_array[mj_idx] = self.joint_pos_array_seq[lab_idx]
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self.stiffness_array[mj_idx] = self.stiffness_array_seq[lab_idx]
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self.damping_array[mj_idx] = self.damping_array_seq[lab_idx]
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# 设置其他参数
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self.kps_lab = self.stiffness_array_seq
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self.kds_lab = self.damping_array_seq
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self.default_angles_lab = self.joint_pos_array_seq
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self.action_scale_lab = self.action_scale
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self.filtered_x_speed = 0
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def _init_onnx_session(self):
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"""初始化ONNX推理会话"""
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try:
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# 配置SessionOptions
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options = ort.SessionOptions()
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# 启用图优化，使用所有可用的优化（包括算子融合等）
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options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
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# 设置执行模式（可选，默认执行模式是顺序执行，但图优化会改变计算图）
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# 设置线程数（根据CPU核心数调整）
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# 建议设置为CPU物理核心数（非超线程数），因为超线程可能不会带来线性提升
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options.intra_op_num_threads = 1 # 设置计算图中的运算符内部并行线程数
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options.inter_op_num_threads = 1 # 设置多个运算符之间的并行线程数（如果模型有多个分支）
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# 启用内存优化（避免重复分配内存）
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options.enable_mem_pattern = False # 对于固定输入大小，可以设为False以避免内存规划的开销
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options.enable_mem_reuse = True # 启用内存重用机制
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self.ort_session_ = ort.InferenceSession(self.model_path, options, providers=['CPUExecutionProvider'])
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print(f"[{self.log_prefix}-ONNX] ONNX model loaded successfully: {self.model_path}")
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except Exception as e:
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print(f"[{self.log_prefix}] Failed to load ONNX model: {e}")
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self.ort_session_ = None
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def on_enter(self):
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"""进入WALKAMP状态"""
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self._reset_internal_state()
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print(f"[{self.log_prefix}] enter")
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self.is_first_obs_ = True
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self.is_first_action_ = True
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self._warmup_inference_counter = 0
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self.timer_gait_ = 0.0
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if self.robot_data_ is not None:
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try:
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self._warm_start_pose = self.robot_data_.get_joint_pos().copy()
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except Exception:
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self._warm_start_pose.fill(0.0)
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else:
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self._warm_start_pose.fill(0.0)
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def run(self, flag: ControlFlag):
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"""运行WALKAMP状态 - 与C++版本完全一致"""
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print(f"[{self.log_prefix}] run")
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# Only run policy inference every decimation_ steps
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gait = gait_phase(
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self.timer_gait_,
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self.gait_cycle,
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self.left_theta_offset,
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self.right_theta_offset,
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self.left_phase_ratio,
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self.right_phase_ratio,
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).astype(np.float32)
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if int(self.robot_data_.time_now_ / self.dt_) % self.decimation_ == 0:
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# print(f"[FSMStateWALKAMP] Gait phase: {gait}")
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self.compute_observation(flag,gait)
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self.compute_actions()
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# lab 顺序目标角 23 维
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target_dof_pos_lab = self.actions_ * self.action_scale_lab + self.default_angles_lab
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# 拿一份当前 mj 顺序的关节角（或你原来用的 default 也行）
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target_dof_pos_mj = self.robot_data_.get_joint_pos().copy()
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# 只更新 23 个受控 DOF
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target_dof_pos_mj[self.lab2mj] = target_dof_pos_lab
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commanded_pos = target_dof_pos_mj
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if self._warm_start_steps > 0 and self._warmup_inference_counter < self._warm_start_steps:
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self._warmup_inference_counter += 1
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blend = self._warmup_inference_counter / float(self._warm_start_steps)
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commanded_pos = (1.0 - blend) * self._warm_start_pose + blend * target_dof_pos_mj
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base = self.robot_data_.q_d_.shape[0] - self.motor_num_
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self.robot_data_.q_d_[base:base + len(self.joint_xml)] = commanded_pos
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self.robot_data_.q_dot_d_[base:base + len(self.joint_xml)] = 0.0
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self.robot_data_.tau_d_[base:base + len(self.joint_xml)] = 0.0
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self.last_actions_[:] = self.actions_
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self.timer_gait_ += self.dt_
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self.robot_data_.joint_kp_p_[:len(self.joint_xml)] = self.stiffness_array
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self.robot_data_.joint_kd_p_[:len(self.joint_xml)] = self.damping_array
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def compute_observation(self, flag: ControlFlag, gait):
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roll, pitch, yaw = (
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float(self.robot_data_.imu_data_[2]),
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float(self.robot_data_.imu_data_[1]),
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float(self.robot_data_.imu_data_[0]),
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)
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quat_wxyz = self.euler_to_quaternion_scipy(roll, pitch, yaw)
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q_xyzw = np.array([quat_wxyz[1], quat_wxyz[2], quat_wxyz[3], quat_wxyz[0]], dtype=np.float32)
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gravity_init = self.quat_rotate_inverse_numpy(q_xyzw, np.array([0.,0.,-1.], dtype=np.float32))
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x_speed_command, y_speed_command, yaw_speed_command = self.robot_data_.get_walk_cmd()
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new_filtered_x_speed = 1 * x_speed_command + (1 - 1) * self.filtered_x_speed
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change = new_filtered_x_speed - self.filtered_x_speed
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change = np.clip(change, -0.005, 0.005)
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self.filtered_x_speed = self.filtered_x_speed + change
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command = np.concatenate([
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np.array([
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x_speed_command,
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y_speed_command,
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yaw_speed_command,
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], dtype=np.float32),
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])
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print(f'Input command: {command}')
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gyro = np.array([
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self.robot_data_.imu_data_[3],
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self.robot_data_.imu_data_[4],
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self.robot_data_.imu_data_[5]
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], dtype=np.float32) * self.ang_vel_scale_
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q_mj = self.robot_data_.get_joint_pos()
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qdot_mj = self.robot_data_.get_joint_vel()
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ang_vel = self.robot_data_.get_angular_velocity()
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q_mj = self.robot_data_.get_joint_pos() # mj 顺序，长度 29
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dq_mj = self.robot_data_.get_joint_vel()
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# 只取 23 个受控关节，变成 lab 顺序
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qj = q_mj[self.lab2mj]
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dqj = dq_mj[self.lab2mj]
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qj = qj - self.default_angles_lab
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# Observation = ang_vel(3) + gravity(3) + command(9) + q(23) + dq(23) + action(23) = 84
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proprio = np.concatenate([
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ang_vel , # 3 elements
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gravity_init,
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command,
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qj,
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dqj,
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self.last_actions_,
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gait
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])
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# History buffer management exactly like C++
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if self.is_first_obs_:
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for i in range(self.num_hist_):
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start_idx = i * self.obs_size_
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end_idx = start_idx + self.obs_size_
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self.proprio_hist_buf_[start_idx:end_idx] = proprio
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self.is_first_obs_ = False
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else:
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# Shift history: head((num_hist-1)*obs_size) = tail((num_hist-1)*obs_size)
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shift_size = (self.num_hist_ - 1) * self.obs_size_
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self.proprio_hist_buf_[:shift_size] = self.proprio_hist_buf_[self.obs_size_:]
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self.proprio_hist_buf_[shift_size:] = proprio
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# Clip observations exactly like C++
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self.observations_ = np.clip(self.proprio_hist_buf_, -self.clip_obs_, self.clip_obs_)
|
||
|
||
|
||
@staticmethod
|
||
def euler_to_quaternion_scipy(roll, pitch, yaw, degrees=False):
|
||
r = Rotation.from_euler('xyz', [roll, pitch, yaw], degrees=degrees)
|
||
q_xyzw = r.as_quat()
|
||
return np.array([q_xyzw[3], q_xyzw[0], q_xyzw[1], q_xyzw[2]], dtype=np.float32)
|
||
|
||
@staticmethod
|
||
def quat_rotate_inverse_numpy(q_xyzw, v):
|
||
q_w = q_xyzw[3]
|
||
q_v = q_xyzw[:3]
|
||
a = v * (2.0 * q_w * q_w - 1.0)
|
||
b = np.cross(q_v, v) * (2.0 * q_w)
|
||
c = q_v * (2.0 * np.dot(q_v, v))
|
||
return a - b + c
|
||
def compute_actions(self):
|
||
if self.ort_session_ is None:
|
||
return
|
||
|
||
try:
|
||
# Prepare input tensor
|
||
input_data = self.observations_.reshape(1, -1).astype(np.float32)
|
||
|
||
# ONNX inference
|
||
input_name = self.ort_session_.get_inputs()[0].name
|
||
outputs = self.ort_session_.run(None, {input_name: input_data})
|
||
|
||
# Extract and clip actions exactly like C++
|
||
output_data = outputs[0][0]
|
||
for i in range(self.action_num_):
|
||
self.actions_[i] = np.clip(output_data[i], -self.clip_act_, self.clip_act_)
|
||
|
||
if self.is_first_action_:
|
||
print(f"[{self.log_prefix}-ONNX] First Observation:")
|
||
for i in range(self.obs_size_):
|
||
print(f"{self.observations_[i]:.6f} ", end="")
|
||
print()
|
||
self.is_first_action_ = False
|
||
|
||
except Exception as e:
|
||
print(f"[{self.log_prefix}] ONNX Runtime inference error: {e}")
|
||
|
||
def on_exit(self):
|
||
"""退出WALKAMP状态"""
|
||
print(f"[{self.log_prefix}] exit")
|
||
# 关掉 obs 日志文件
|
||
if getattr(self, "obs_log_file", None) is not None:
|
||
try:
|
||
self.obs_log_file.flush()
|
||
self.obs_log_file.close()
|
||
print(f"[{self.log_prefix}] obs log saved to {self.obs_log_path}")
|
||
except Exception as e:
|
||
print(f"[{self.log_prefix}] failed to close obs log: {e}")
|
||
self.obs_log_file = None
|
||
|
||
def check_transition(self, flag: ControlFlag) -> FSMStateName:
|
||
"""检查状态转换"""
|
||
if flag.fsm_state_command == "gotoSTOP":
|
||
return FSMStateName.STOP
|
||
elif flag.fsm_state_command == "gotoWALKAMP":
|
||
return FSMStateName.WALKAMP
|
||
elif flag.fsm_state_command == "gotoZERO":
|
||
return FSMStateName.ZERO
|
||
else:
|
||
return None # 无状态转换
|