From 088f5f6af02861d31aefc0f79deb34146e8fc913 Mon Sep 17 00:00:00 2001 From: Weiyu1105 <86831759+Weiyu1105@users.noreply.github.com> Date: Mon, 4 Aug 2025 20:54:26 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20=E6=94=AF=E6=8F=B4=E6=A8=A1=E5=9E=8B?= =?UTF-8?q?=E8=BC=B8=E5=87=BA=E6=A8=A1=E5=BC=8F=20support=20model=20output?= =?UTF-8?q?=20mode?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- config.yaml | 12 ++++++++---- hardware_controller.py | 10 ++++++++-- policy.py | 6 +++++- simulation_controller.py | 9 ++++++++- 4 files changed, 29 insertions(+), 8 deletions(-) diff --git a/config.yaml b/config.yaml index 49345e4..7c789f1 100644 --- a/config.yaml +++ b/config.yaml @@ -13,8 +13,9 @@ mujoco_model_file: "assets/scene_mjx.xml" # ONNX 模型庫 (ONNX Model Library) onnx_models: - stable_walk: + stable_walk: path: "models/pupper_ppo_policy_30965760_tf_converted_穩定步態版.onnx" + output_mode: "offset" # 模型輸出為偏移量 # 【新】為每個模型指定觀察配方 observation_recipe: - 'linear_velocity' @@ -25,9 +26,10 @@ onnx_models: - 'last_action' - 'commands' - agile_model: + agile_model: path: "models/pupper_ppo_policy_200540160_tf_converted.onnx" # 假設 agile_model 使用與 stable_walk 相同的配方 + output_mode: "offset" observation_recipe: - 'linear_velocity' - 'angular_velocity' @@ -37,9 +39,10 @@ onnx_models: - 'last_action' - 'commands' - new_high_level: + new_high_level: path: "models/pupper_ppo_policy_203161600_tf_v2.3_converted.onnx" # 【新】使用新的觀察配方 + output_mode: "offset" observation_recipe: - 'angular_velocity' - 'gravity_vector' @@ -49,8 +52,9 @@ onnx_models: - 'last_action' - 'commands' - new_e2e: + new_e2e: path: "models/pupper_ppo_policy_e2e_203161600.onnx" + output_mode: "absolute" # 此模型直接輸出絕對角度 # e2e 模型也使用新的觀察配方 observation_recipe: - 'angular_velocity' diff --git a/hardware_controller.py b/hardware_controller.py index 9158f54..ff13ffe 100644 --- a/hardware_controller.py +++ b/hardware_controller.py @@ -274,8 +274,14 @@ def _control_loop(self): # 【保留】更新 last_action self.hw_state.last_action[:] = action_raw - # 【保留】生成發送給Teensy的文字指令 - final_command = default_pose_hardware + action_raw * self.global_state.tuning_params.action_scale + # 【修改】根據模型設定決定最終控制模式 + model_cfg = self.policy.get_active_model_config() # 取得當前策略的設定 + if model_cfg.get('output_mode') == 'absolute': + # 若模型直接輸出絕對角度，則不加上 default pose + final_command = action_raw * self.global_state.tuning_params.action_scale + else: + # 預設為偏移量模式，需要加上 default pose + final_command = default_pose_hardware + action_raw * self.global_state.tuning_params.action_scale action_str = ' '.join(f"{a:.4f}" for a in final_command) command_to_send = f"move all {action_str}\n" diff --git a/policy.py b/policy.py index a3be771..ff3b25a 100644 --- a/policy.py +++ b/policy.py @@ -5,7 +5,7 @@ import os import time from collections import deque -from typing import TYPE_CHECKING, List, Dict +from typing import TYPE_CHECKING, List, Dict, Any # 為了型別提示，避免循環匯入 if TYPE_CHECKING: @@ -118,6 +118,10 @@ def get_active_recipe(self) -> List[str]: """一個輔助函式，返回當前主要策略所使用的觀察配方，主要供 HardwareController 使用。""" return self.model_recipes.get(self.primary_policy_name, []) + def get_active_model_config(self) -> Dict[str, Any]: + """取得目前主策略的完整設定字典 (包含自訂 output_mode 等資訊)。""" + return self.config.onnx_models.get(self.primary_policy_name, {}) + def select_target_policy(self, target_name: str): """(由鍵盤觸發) 選擇一個目標策略並開始平滑轉換。""" # 檢查目標模型是否存在 diff --git a/simulation_controller.py b/simulation_controller.py index 793b273..660a928 100644 --- a/simulation_controller.py +++ b/simulation_controller.py @@ -217,7 +217,14 @@ def _simulation_step(self) -> None: with self.state.lock: final_ctrl = self.sim.default_pose + self.state.joint_test_offsets else: - final_ctrl = self.sim.default_pose + action_final * tuning_params.action_scale + # 【修改】根據模型的輸出模式決定是否加上 default pose + model_cfg = self.policy_manager.get_active_model_config() + if model_cfg.get('output_mode') == 'absolute': + # 模型輸出為絕對角度 + final_ctrl = action_final * tuning_params.action_scale + else: + # 預設：模型輸出為偏移量 + final_ctrl = self.sim.default_pose + action_final * tuning_params.action_scale self.sim.apply_position_control(final_ctrl, tuning_params)