File size: 7,540 Bytes
d8290eb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 | """Per-robot deployment specifications (G1 / H2 / A3).
Every number here mirrors the training configuration of the corresponding
``PingPong-One-Step-<ROBOT>-FB-OnlineCLoSD-Ball-DualCritic`` task:
* base task: ``tasks/tracking/tracking_pingpong_online_closd_ball_env_cfg_FB.py``
* robot overrides: ``tasks/tracking/config/{h2,a3}/flat_env_cfg.py`` via
``configure_online_closd_robot``; G1 uses the base values.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from pathlib import Path
from .paths import assets_dir, package_dir, resolve
# The 14 reference bodies tracked by the policy (tracking_pingpong_fk_ref_env_cfg_FB.REFERENCE_BODY_NAMES).
REFERENCE_BODY_NAMES: tuple[str, ...] = (
"pelvis",
"left_hip_roll_link",
"left_knee_link",
"left_ankle_roll_link",
"right_hip_roll_link",
"right_knee_link",
"right_ankle_roll_link",
"torso_link",
"left_shoulder_roll_link",
"left_elbow_link",
"left_wrist_yaw_link",
"right_shoulder_roll_link",
"right_elbow_link",
"right_wrist_yaw_link",
)
@dataclass(frozen=True)
class EmbodimentSpec:
name: str
num_actions: int
sim2sim_module: str # module in sim2sim_tt providing <Robot>Env + gain/armature helpers
env_class: str
armature_helper: str | None # optional build_<robot>_armature_for_joint_names in that module
mjcf: str # relative to assets/robot_description
closd_bundle: str # bundle-relative
ready_pose: str # bundle-relative
policy: str # bundle-relative default policy
paddle_body_name: str
paddle_offset_xyz: tuple[float, float, float]
paddle_offset_rpy: tuple[float, float, float]
paddle_half_width: float
paddle_half_height: float
nominal_root_z: float
hit_x_range: tuple[float, float]
hit_y_range: tuple[float, float]
hit_z_range: tuple[float, float]
condition_time_range: tuple[float, float]
condition_lateral_range: tuple[float, float]
condition_forward_range: tuple[float, float]
condition_height_range: tuple[float, float]
foot_body_names: tuple[str, str]
# The training articulation has no joint damping / Coulomb friction beyond the
# PD actuator model. The MJCFs declare some (A3: 0.5-1.5 N路m路s/rad and
# 0.4-1.7 N路m on every joint; H2 0.05 / 0.2; G1 friction 0.1), so they are
# neutralised by default; ``--keep-mjcf-joint-dynamics`` keeps them.
zero_mjcf_joint_damping: bool = True
zero_mjcf_joint_frictionloss: bool = True
# shared by all robots (base config)
hit_time_range: tuple[float, float] = (0.45, 0.55)
post_hit_resample_delay_range: tuple[float, float] = (0.15, 0.70)
long_pause_prob: float = 0.2
long_pause_range: tuple[float, float] = (1.5, 3.0)
target_landing_x_range: tuple[float, float] = (2.30, 3.10)
target_landing_y_range: tuple[float, float] = (-0.60, 0.60)
max_delta_hit_y: float = 0.7
stroke_split_relative_y: float = -0.125
frozen_blend_time_s: float = 0.24
frozen_exit_blend_time_s: float = 0.24
closd_fps: float = 50.0
control_dt: float = 0.02
sim_dt: float = 0.005
reference_body_names: tuple[str, ...] = REFERENCE_BODY_NAMES
frozen_task_command: tuple[float, ...] = field(default=(-0.59, 0.40, 0.0, 1.0, -3.0, 0.0, 0.2, 2.87, 0.0))
@property
def hit_plane_x(self) -> float:
return 0.5 * (self.hit_x_range[0] + self.hit_x_range[1])
def mjcf_path(self) -> Path:
return resolve(self.mjcf) if Path(self.mjcf).is_absolute() or (package_dir() / self.mjcf).exists() else assets_dir() / self.mjcf
def bundle_path(self) -> Path:
return resolve(self.closd_bundle)
def ready_pose_path(self) -> Path:
return resolve(self.ready_pose)
def policy_path(self) -> Path:
return resolve(self.policy)
def frozen_command(self) -> tuple[float, ...]:
cmd = list(self.frozen_task_command)
cmd[1] = self.hit_plane_x # configure_online_closd_robot rewrites element 1 to hit_plane_x
return tuple(cmd)
EMBODIMENTS: dict[str, EmbodimentSpec] = {
"g1": EmbodimentSpec(
name="g1",
num_actions=29,
sim2sim_module="play_tt_mujoco_g1_fb",
env_class="G1Env",
armature_helper=None,
mjcf="g1/mjcf/g1_full_mesh_with_hand_tt.xml",
closd_bundle="closd/G1/model_80000.closd-runtime.pt",
ready_pose="ready_pose/G1/frozen_closd_ready_pose_toeout_lowload.npz",
policy="policies/g1/policy.onnx",
paddle_body_name="right_wrist_yaw_link",
paddle_offset_xyz=(0.18522, 0.033288, 0.089488),
paddle_offset_rpy=(3.1415926, -0.524, -3.1415926),
paddle_half_width=0.0763,
paddle_half_height=0.0763,
nominal_root_z=0.78,
hit_x_range=(0.35, 0.35),
hit_y_range=(-0.76, 0.76),
hit_z_range=(0.70, 1.30),
condition_time_range=(0.40, 0.60),
condition_lateral_range=(-1.10, 0.45),
condition_forward_range=(-0.05, 0.90),
condition_height_range=(0.70, 1.12),
foot_body_names=("left_ankle_roll_link", "right_ankle_roll_link"),
),
"h2": EmbodimentSpec(
name="h2",
num_actions=31,
sim2sim_module="play_tt_mujoco_h2_fb",
env_class="H2Env",
armature_helper="build_h2_armature_for_joint_names",
mjcf="h2/mjcf/h2_pingpong.xml",
closd_bundle="closd/H2/model_80000.closd-runtime.pt",
ready_pose="ready_pose/H2/frozen_closd_ready_pose_toeout_lowload.npz",
policy="policies/h2/h2_stageC_model_10500.onnx",
paddle_body_name="right_wrist_yaw_link",
paddle_offset_xyz=(0.19572, 0.036449, 0.08931579),
paddle_offset_rpy=(0.0, 0.0, 0.0),
paddle_half_width=0.0815,
paddle_half_height=0.0855,
nominal_root_z=1.0,
hit_x_range=(0.35, 0.45),
hit_y_range=(-0.76, 0.76),
hit_z_range=(0.80, 1.50),
condition_time_range=(0.40, 0.60),
condition_lateral_range=(-1.35, 0.47),
condition_forward_range=(-0.08, 1.16),
condition_height_range=(0.99, 1.45),
foot_body_names=("left_ankle_pitch_link", "right_ankle_pitch_link"),
),
"a3": EmbodimentSpec(
name="a3",
num_actions=29,
sim2sim_module="play_tt_mujoco_a3_fb",
env_class="A3Env",
armature_helper="build_a3_armature_for_joint_names",
mjcf="a3/mjcf/a3_t2d5_pingpong.xml",
closd_bundle="closd/A3/model_80000.closd-runtime.pt",
ready_pose="ready_pose/A3/frozen_closd_ready_pose_toeout_lowload.npz",
policy="policies/a3/policy.onnx",
paddle_body_name="right_wrist_yaw_link",
paddle_offset_xyz=(0.21021, 0.032078, 0.032036),
paddle_offset_rpy=(0.0, 0.0, 0.0),
paddle_half_width=0.0763,
paddle_half_height=0.0763,
nominal_root_z=1.0,
hit_x_range=(0.35, 0.45),
hit_y_range=(-0.76, 0.76),
hit_z_range=(0.70, 1.40),
condition_time_range=(0.40, 0.60),
condition_lateral_range=(-1.35, 0.40),
condition_forward_range=(-0.10, 1.10),
condition_height_range=(0.78, 1.26),
foot_body_names=("left_ankle_roll_link", "right_ankle_roll_link"),
),
}
def load_embodiment(name: str) -> EmbodimentSpec:
key = name.lower()
if key not in EMBODIMENTS:
raise KeyError(f"unknown embodiment '{name}', expected one of {sorted(EMBODIMENTS)}")
return EMBODIMENTS[key]
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