File size: 9,865 Bytes
cb94537 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 |
# ------------------------------------------------------------------------------
# Copyright 2025 2toINF (https://github.com/2toINF)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ------------------------------------------------------------------------------
from __future__ import annotations
from typing import Iterable, Tuple, Dict, Type
import torch
import torch.nn as nn
# =============================================================================
# Registry
# =============================================================================
ACTION_REGISTRY: Dict[str, Type["BaseActionSpace"]] = {}
def register_action(name: str):
"""Decorator for registering a new action space."""
def _wrap(cls):
key = name.lower()
if key in ACTION_REGISTRY:
raise KeyError(f"ActionSpace '{key}' already registered -> {ACTION_REGISTRY[key]}")
ACTION_REGISTRY[key] = cls
cls.name = key
return cls
return _wrap
def build_action_space(name: str, **kwargs) -> "BaseActionSpace":
"""Instantiate a registered action space by name."""
key = name.lower()
if key not in ACTION_REGISTRY:
raise KeyError(f"Unknown action space '{name}'. Available: {list(ACTION_REGISTRY.keys())}")
return ACTION_REGISTRY[key](**kwargs)
# =============================================================================
# Base class
# =============================================================================
class BaseActionSpace(nn.Module):
"""
Abstract base class for all action-space definitions.
Each subclass defines:
- `dim_action`: dimension of the action vector.
- `gripper_idx`: indices of gripper channels.
- `compute_loss(pred, target)`: supervised loss for this space.
- `preprocess(proprio, action, mode)`: pre-step modifications.
- `postprocess(action)`: post-step corrections (e.g. apply sigmoid).
"""
name: str = "base"
dim_action: int = 0
gripper_idx: Tuple[int, ...] = ()
def __init__(self):
super().__init__()
# ---------------------------------------------------------------------
# Core supervised loss
# ---------------------------------------------------------------------
def compute_loss(self, pred: torch.Tensor, target: torch.Tensor) -> Dict[str, torch.Tensor]:
raise NotImplementedError
def forward(self, pred: torch.Tensor, target: torch.Tensor) -> Dict[str, torch.Tensor]:
"""Alias for compute_loss."""
return self.compute_loss(pred, target)
# ---------------------------------------------------------------------
# Space-level hooks
# ---------------------------------------------------------------------
def preprocess(
self,
proprio: torch.Tensor,
action: torch.Tensor,
mode: str = "train",
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Default: return unchanged."""
return proprio, action
def postprocess(self, action: torch.Tensor) -> torch.Tensor:
"""Default: return unchanged."""
return action
# =============================================================================
# Utilities
# =============================================================================
def _ensure_indices_valid(D: int, idx: Iterable[int], name: str) -> None:
bad = [i for i in idx if i < 0 or i >= D]
if bad:
raise IndexError(f"{name} contains out-of-range indices {bad} for action dim D={D}")
# =============================================================================
# Implementations
# =============================================================================
@register_action("ee6d")
class EE6DActionSpace(BaseActionSpace):
"""End-effector layout with xyz, 6D rotation, and gripper channels."""
dim_action = 20
gripper_idx = (9, 19)
GRIPPER_SCALE = 1.0
XYZ_SCALE = 500.0
ROT_SCALE = 10.0
POS_IDX_1 = (0, 1, 2)
POS_IDX_2 = (10, 11, 12)
ROT_IDX_1 = (3, 4, 5, 6, 7, 8)
ROT_IDX_2 = (13, 14, 15, 16, 17, 18)
def __init__(self):
super().__init__()
self.mse = nn.MSELoss()
self.bce = nn.BCEWithLogitsLoss()
def compute_loss(self, pred, target):
assert pred.shape == target.shape, "pred/target shapes must match"
B, T, D = pred.shape
_ensure_indices_valid(D, self.gripper_idx, "gripper_idx")
# Gripper BCE
g_losses = [self.bce(pred[:, :, gi], target[:, :, gi]) for gi in self.gripper_idx]
gripper_loss = sum(g_losses) / len(self.gripper_idx) * self.GRIPPER_SCALE
# XYZ position
pos_loss = (
self.mse(pred[:, :, self.POS_IDX_1], target[:, :, self.POS_IDX_1]) +
self.mse(pred[:, :, self.POS_IDX_2], target[:, :, self.POS_IDX_2])
) * self.XYZ_SCALE
# Rotation 6D
rot_loss = (
self.mse(pred[:, :, self.ROT_IDX_1], target[:, :, self.ROT_IDX_1]) +
self.mse(pred[:, :, self.ROT_IDX_2], target[:, :, self.ROT_IDX_2])
) * self.ROT_SCALE
return {
"position_loss": pos_loss,
"rotate6D_loss": rot_loss,
"gripper_loss": gripper_loss,
}
def preprocess(self, proprio, action, mode="train"):
"""Zero-out gripper channels in proprio/action."""
proprio_m = proprio.clone()
action_m = action.clone()
proprio_m[..., self.gripper_idx] = 0.0
action_m[..., self.gripper_idx] = 0.0
return proprio_m, action_m
def postprocess(self, action: torch.Tensor) -> torch.Tensor:
"""Apply sigmoid to gripper logits."""
if action.size(-1) > max(self.gripper_idx):
action[..., self.gripper_idx] = torch.sigmoid(action[..., self.gripper_idx])
return action
@register_action("joint")
class JointActionSpace(BaseActionSpace):
"""Joint-space layout with joints + gripper only."""
dim_action = 14
gripper_idx = (6, 13)
GRIPPER_SCALE = 0.1
JOINTS_SCALE = 1.0
def __init__(self):
super().__init__()
self.mse = nn.MSELoss()
self.bce = nn.BCEWithLogitsLoss()
def compute_loss(self, pred, target):
assert pred.shape == target.shape
B, T, D = pred.shape
_ensure_indices_valid(D, self.gripper_idx, "gripper_idx")
g_losses = [self.bce(pred[:, :, gi], target[:, :, gi]) for gi in self.gripper_idx]
gripper_loss = sum(g_losses) / len(self.gripper_idx) * self.GRIPPER_SCALE
joints_idx = tuple(i for i in range(D) if i not in set(self.gripper_idx))
joints_loss = self.mse(pred[:, :, joints_idx], target[:, :, joints_idx]) * self.JOINTS_SCALE
return {
"joints_loss": joints_loss,
"gripper_loss": gripper_loss,
}
def preprocess(self, proprio, action, mode="train"):
"""Zero-out gripper channels in proprio/action."""
proprio_m = proprio.clone()
action_m = action.clone()
proprio_m[..., self.gripper_idx] = 0.0
action_m[..., self.gripper_idx] = 0.0
return proprio_m, action_m
def postprocess(self, action: torch.Tensor) -> torch.Tensor:
"""Apply sigmoid to gripper logits."""
if action.size(-1) > max(self.gripper_idx):
action[..., self.gripper_idx] = torch.sigmoid(action[..., self.gripper_idx])
return action
@register_action("agibot_ee6d")
class AGIBOTEE6DActionSpace(BaseActionSpace):
"""AGI-bot variant of EE6DActionSpace using MSE for all components."""
dim_action = 20
gripper_idx = (9, 19)
GRIPPER_SCALE = 10.0
XYZ_SCALE = 500.0
ROT_SCALE = 10.0
POS_IDX_1 = (0, 1, 2)
POS_IDX_2 = (10, 11, 12)
ROT_IDX_1 = (3, 4, 5, 6, 7, 8)
ROT_IDX_2 = (13, 14, 15, 16, 17, 18)
def __init__(self):
super().__init__()
self.mse = nn.MSELoss()
def compute_loss(self, pred, target):
assert pred.shape == target.shape
B, T, D = pred.shape
_ensure_indices_valid(D, self.gripper_idx, "gripper_idx")
gripper_loss = self.mse(pred[:, :, self.gripper_idx], target[:, :, self.gripper_idx]) * self.GRIPPER_SCALE
pos_loss = (
self.mse(pred[:, :, self.POS_IDX_1], target[:, :, self.POS_IDX_1]) +
self.mse(pred[:, :, self.POS_IDX_2], target[:, :, self.POS_IDX_2])
) * self.XYZ_SCALE
rot_loss = (
self.mse(pred[:, :, self.ROT_IDX_1], target[:, :, self.ROT_IDX_1]) +
self.mse(pred[:, :, self.ROT_IDX_2], target[:, :, self.ROT_IDX_2])
) * self.ROT_SCALE
return {
"position_loss": pos_loss,
"rotate6D_loss": rot_loss,
"gripper_loss": gripper_loss,
}
def preprocess(self, proprio, action, mode="train"):
"""No preprocessing applied in AGIBOT variant."""
return proprio, action
def postprocess(self, action: torch.Tensor) -> torch.Tensor:
"""AGIBOT does not postprocess."""
return action
# =============================================================================
# Exports
# =============================================================================
__all__ = [
"BaseActionSpace",
"build_action_space",
"register_action",
"EE6DActionSpace",
"JointActionSpace",
"AGIBOTEE6DActionSpace",
"ACTION_REGISTRY",
]
|