3D-Diffusion-Policy

  • 符号
    • T: 预测轨迹长度
  • eval_policy.py 调用 deploy_policy.py:eval(),其调用 RobotRunner.get_action()
  • RobotRunner.get_action() (robot_runner.py)
    • obs = self.get_n_steps_obs()
      • obs <- update_obs() 就是 append <- Base_task.get_obs()
        • [ai]
        • observation - 包含来自各种相机的观察数据
          • 相机数据包括 head_camera, left_camera, right_camera, front_camera
          • 每个相机可以包含以下数据(取决于 data_type 设置):
            • rgb - RGB图像数据
            • mesh_segmentation - 网格分割数据
            • actor_segmentation - 实体分割数据
            • depth - 深度图像数据
            • 相机内参和外参矩阵
        • pointcloud - 点云数据
          • 如果 data_type['pointcloud'] 为 True,则包含点云数据
          • 可以选择是否合并多个相机的点云数据
        • joint_action - 机器人关节状态
          • 如果 data_type['qpos'] 为 True,包含机器人关节角度
          • 双臂模式下,包含左臂和右臂的关节角度
          • 单臂模式下,仅包含右臂的关节角度
        • endpose - 机器人末端执行器姿态
          • 如果 data_type['endpose'] 为 True,包含末端执行器的位置和姿态
          • 双臂模式下,包含左右两个末端执行器的信息(位置x,y,z,欧拉角roll,pitch,yaw,以及夹爪状态)
          • 单臂模式下,仅包含右臂末端执行器信息
        • vision_tactile - 视觉触觉传感器数据(当 TACTILE_ON 为 True 时)
          • 如果 data_type['vision_tactile'] 为 True,包含触觉传感器的RGB图像数据
      • 随后拿出两个数据并重命名: pointcloud -> point_cloud, joint_action -> agent_pos
      • 得到 obs: Dict
        • each_key => 将最近 n 个观测的 key 在第 0 维度拼接. 形状为 (n_steps, ) + shape_of_the_value
          • n_steps 在参数 yaml 里为 n_obs_steps = 3
      • 在前面 unsqueeze 一个长度为 1 的维度后送进 DP3.predict_action() (应该是因为推理的时候 batch 必是 1)
class DP3:
    predict_action() and  (dp3.py)
        [arg] (obs_dict):
            'point_cloud': (1, 3, 1024, 6)
            'agent_pos': (3, 14) 就是关节角度

        normalize()
        if @?global_cond:
            point_cloud & agent_pos 都送入 DP3Encoder,得到 (3, 192),压扁成 (1, 576)
            mask 就是全部 mask 掉, 所有动作都需要通过扩散模型生成
        else:
            mask 观察特征保持可见
            送入 self.condition_sapmle()
            return. 实测表明一次预测 6 步且会把这 6 步执行完,再预测下 6 步.

        ...
        action_pred: (B, T, action_dim) e.g. (1, 8, 14) 

        start = To - 1
        end = start + self.n_action_steps
        action = action_pred[:, start:end] e.g. (1, 6, 14)

    condition_sample():
        生成的 traj shape 是 (B, T, action_dim) = (1, 8, 14)
        每个去噪步 model(sample=trajectory, timestep=t, local_cond=local_cond(必为 None), global_cond=global_cond)
        model is @ConditionalUnet1D.forward():
[ConditionalUnet1D.forward()]:
    ...

    timestep: (形状 (B, ) or int)
    encoding: (SinusoidalPosEmb, Linear, Mish, Linear)

    if global_cond: global_feature = cat([timestep_embed, global_cond], axis=-1) }

    [Downsample]
    x = sample (生成 trajactory)
    for idx, (@resnet, @resnet2, @downsample) in enumerate(self.down_modules):
        if self.use_down_condition:
            x = resnet(x, global_feature)
            if idx == 0 and len(h_local) > 0:
                x = x + h_local[0]
            x = resnet2(x, global_feature)
        else:
            x = resnet(x)
            if idx == 0 and len(h_local) > 0:
                x = x + h_local[0]
            x = resnet2(x)
        h.append(x)
        x = downsample(x)

    [mid_module (@ConditionalResidualBlock1D)]
    ...

    [Upsample]
    对称的

    [return]
    x = self.final_conv(x)
    return x

[resnet] = @ConditionalResidualBlock1D(
    dim_in, dim_out, cond_dim=cond_dim,
    kernel_size=kernel_size, n_groups=n_groups,
    condition_type=condition_type
),
[resnet2] = ConditionalResidualBlock1D(
    dim_out, dim_out, cond_dim=cond_dim,
    kernel_size=kernel_size, n_groups=n_groups,
    condition_type=condition_type
),
[downsample] = Downsample1d(dim_out) if not is_last else nn.Identity()

[ConditionalResidualBlock1D].forward():
    out = Conv1dBlock() (x)
    if `cross_attention_add`
        embed = CrossAttention() (x)
        out = out + embed (是 tensor 值加)
    out = another Conv1dBlock() (x)
    out = out + self.residual_conv(x)
    return out
DP3Encoder:
    'point_cloud' => B x N x 3的 点云 (B: batch) = (3, 1024, 3) 送入 PointNetEncoderXYZ:

class PointNetEncoderXYZ
    MLP: [Linear + LayerNorm + ReLU] x 3
         channels: 3 => 64 => 128 => 256 => max => Linear + LayerNorm (128)

    forward():
        (B, N, 3) = (3, 1024, 3)
        mlp => (3, 1024, 256)
        max => (3, 256)
        Linear + LayerNorm => (3, 128)
        self.state_mlp: 简单的 MLP (Linear + ReLU). state_mlp_size = (64, 64).
        最后 cat 成 (3, 192)