stompy design

sim/env.py

@@ -6,3 +6,10 @@
 
 def model_dir() -> Path:
     return Path(os.environ.get("MODEL_DIR", "models"))
+
+
+def stompy_urdf_path() -> Path:
+    stompy_path = model_dir() / "robots" / "stompy" / "robot.urdf"
+    if not stompy_path.exists():
+        raise FileNotFoundError(f"URDF file not found: {stompy_path}")
+    return stompy_path.resolve()

sim/humanoid_gym/stompy_config.py

@@ -0,0 +1,230 @@
+"""Configuration for the Stompy humanoid robot."""
+
+from humanoid.envs.base.legged_robot_config import LeggedRobotCfg, LeggedRobotCfgPPO
+
+from sim.env import stompy_urdf_path
+
+
+class StompyConfig(LeggedRobotCfg):
+    """Configuration class for the XBotL humanoid robot."""
+
+    class env(LeggedRobotCfg.env):  # noqa: N801
+        frame_stack = 15
+        c_frame_stack = 3
+        num_single_obs = 47
+        num_observations = int(frame_stack * num_single_obs)
+        single_num_privileged_obs = 73
+        num_privileged_obs = int(c_frame_stack * single_num_privileged_obs)
+        num_actions = 12
+        num_envs = 4096
+        episode_length_s = 24  # episode length in seconds
+        use_ref_actions = False
+
+    class safety:  # noqa: N801
+        # safety factors
+        pos_limit = 1.0
+        vel_limit = 1.0
+        torque_limit = 0.85
+
+    class asset(LeggedRobotCfg.asset):  # noqa: N801
+        file = str(stompy_urdf_path())
+
+        name = "Stompy"
+        foot_name = "ankle_roll"
+        knee_name = "knee"
+
+        terminate_after_contacts_on = ["base_link"]
+        penalize_contacts_on = ["base_link"]
+        self_collisions = 0  # 1 to disable, 0 to enable...bitwise filter
+        flip_visual_attachments = False
+        replace_cylinder_with_capsule = False
+        fix_base_link = False
+
+    class terrain(LeggedRobotCfg.terrain):  # noqa: N801
+        mesh_type = "plane"
+        # mesh_type = 'trimesh'
+        curriculum = False
+        # rough terrain only:
+        measure_heights = False
+        static_friction = 0.6
+        dynamic_friction = 0.6
+        terrain_length = 8.0
+        terrain_width = 8.0
+        num_rows = 20  # number of terrain rows (levels)
+        num_cols = 20  # number of terrain cols (types)
+        max_init_terrain_level = 10  # starting curriculum state
+        # plane; obstacles; uniform; slope_up; slope_down, stair_up, stair_down
+        terrain_proportions = [0.2, 0.2, 0.4, 0.1, 0.1, 0, 0]
+        restitution = 0.0
+
+    class noise:  # noqa: N801
+        add_noise = True
+        noise_level = 0.6  # scales other values
+
+        class noise_scales:  # noqa: N801
+            dof_pos = 0.05
+            dof_vel = 0.5
+            ang_vel = 0.1
+            lin_vel = 0.05
+            quat = 0.03
+            height_measurements = 0.1
+
+    class init_state(LeggedRobotCfg.init_state):  # noqa: N801
+        pos = [0.0, 0.0, 0.95]
+
+        default_joint_angles = {  # = target angles [rad] when action = 0.0
+            "left_leg_yaw_joint": -0.0,
+            "left_leg_roll_joint": -0.0,
+            "left_leg_pitch_joint": 0.0,
+            "left_knee_joint": 0.0,
+            "left_ankle_pitch_joint": 0.0,
+            "left_ankle_roll_joint": 0.0,
+            "right_leg_yaw_joint": 0.0,
+            "right_leg_roll_joint": 0.0,
+            "right_leg_pitch_joint": -0.0,
+            "right_knee_joint": -0.0,
+            "right_ankle_pitch_joint": -0.0,
+            "right_ankle_roll_joint": 0.0,
+        }
+
+    class control(LeggedRobotCfg.control):  # noqa: N801
+        # PD Drive parameters:
+        stiffness = {"leg_roll": 200.0, "leg_pitch": 350.0, "leg_yaw": 200.0, "knee": 350.0, "ankle": 15}
+        damping = {"leg_roll": 10, "leg_pitch": 10, "leg_yaw": 10, "knee": 10, "ankle": 10}
+
+        # action scale: target angle = actionScale * action + defaultAngle
+        action_scale = 0.25
+        # decimation: Number of control action updates @ sim DT per policy DT
+        decimation = 10  # 100hz
+
+    class sim(LeggedRobotCfg.sim):  # noqa: N801
+        dt = 0.001  # 1000 Hz
+        substeps = 1  # 2
+        up_axis = 1  # 0 is y, 1 is z
+
+        class physx(LeggedRobotCfg.sim.physx):  # noqa: N801
+            num_threads = 10
+            solver_type = 1  # 0: pgs, 1: tgs
+            num_position_iterations = 4
+            num_velocity_iterations = 0
+            contact_offset = 0.01  # [m]
+            rest_offset = 0.0  # [m]
+            bounce_threshold_velocity = 0.1  # [m/s]
+            max_depenetration_velocity = 1.0
+            max_gpu_contact_pairs = 2**23  # 2**24 -> needed for 8000 envs and more
+            default_buffer_size_multiplier = 5
+            # 0: never, 1: last sub-step, 2: all sub-steps (default=2)
+            contact_collection = 2
+
+    class domain_rand:  # noqa: N801
+        randomize_friction = True
+        friction_range = [0.1, 2.0]
+        randomize_base_mass = True
+        added_mass_range = [-5.0, 5.0]
+        push_robots = True
+        push_interval_s = 4
+        max_push_vel_xy = 0.2
+        max_push_ang_vel = 0.4
+        dynamic_randomization = 0.02
+
+    class commands(LeggedRobotCfg.commands):  # noqa: N801
+        # Vers: lin_vel_x, lin_vel_y, ang_vel_yaw, heading (in heading mode ang_vel_yaw is recomputed from heading error)
+        num_commands = 4
+        resampling_time = 8.0  # time before command are changed[s]
+        heading_command = True  # if true: compute ang vel command from heading error
+
+        class ranges:  # noqa: N801
+            lin_vel_x = [-0.3, 0.6]  # min max [m/s]
+            lin_vel_y = [-0.3, 0.3]  # min max [m/s]
+            ang_vel_yaw = [-0.3, 0.3]  # min max [rad/s]
+            heading = [-3.14, 3.14]
+
+    class rewards:  # noqa: N801
+        base_height_target = 0.89
+        min_dist = 0.2
+        max_dist = 0.5
+        # put some settings here for LLM parameter tuning
+        target_joint_pos_scale = 0.17  # rad
+        target_feet_height = 0.06  # m
+        cycle_time = 0.64  # sec
+        # if true negative total rewards are clipped at zero (avoids early termination problems)
+        only_positive_rewards = True
+        # tracking reward = exp(error*sigma)
+        tracking_sigma = 5
+        max_contact_force = 700  # forces above this value are penalized
+
+        class scales:  # noqa: N801
+            # reference motion tracking
+            joint_pos = 1.6
+            feet_clearance = 1.0
+            feet_contact_number = 1.2
+            # gait
+            feet_air_time = 1.0
+            foot_slip = -0.05
+            feet_distance = 0.2
+            knee_distance = 0.2
+            # contact
+            feet_contact_forces = -0.01
+            # vel tracking
+            tracking_lin_vel = 1.2
+            tracking_ang_vel = 1.1
+            vel_mismatch_exp = 0.5  # lin_z; ang x,y
+            low_speed = 0.2
+            track_vel_hard = 0.5
+            # base pos
+            default_joint_pos = 0.5
+            orientation = 1.0
+            base_height = 0.2
+            base_acc = 0.2
+            # energy
+            action_smoothness = -0.002
+            torques = -1e-5
+            dof_vel = -5e-4
+            dof_acc = -1e-7
+            collision = -1.0
+
+    class normalization:  # noqa: N801
+        class obs_scales:  # noqa: N801
+            lin_vel = 2.0
+            ang_vel = 1.0
+            dof_pos = 1.0
+            dof_vel = 0.05
+            quat = 1.0
+            height_measurements = 5.0
+
+        clip_observations = 18.0
+        clip_actions = 18.0
+
+
+class XBotLCfgPPO(LeggedRobotCfgPPO):
+    seed = 5
+    runner_class_name = "OnPolicyRunner"  # DWLOnPolicyRunner
+
+    class policy:  # noqa: N801
+        init_noise_std = 1.0
+        actor_hidden_dims = [512, 256, 128]
+        critic_hidden_dims = [768, 256, 128]
+
+    class algorithm(LeggedRobotCfgPPO.algorithm):  # noqa: N801
+        entropy_coef = 0.001
+        learning_rate = 1e-5
+        num_learning_epochs = 2
+        gamma = 0.994
+        lam = 0.9
+        num_mini_batches = 4
+
+    class runner:  # noqa: N801
+        policy_class_name = "ActorCritic"
+        algorithm_class_name = "PPO"
+        num_steps_per_env = 60  # per iteration
+        max_iterations = 3001  # number of policy updates
+
+        # logging
+        save_interval = 100  # check for potential saves every this many iterations
+        experiment_name = "XBot_ppo"
+        run_name = ""
+        # load and resume
+        resume = False
+        load_run = -1  # -1 = last run
+        checkpoint = -1  # -1 = last saved model
+        resume_path = None  # updated from load_run and chkpt