用LSTM设计一个做家务的机器人，比如帮我浇花，擦桌子，做饭

用LSTM这种特殊的循环神经网络RNN帮我设计一个做家务的机器人，比如帮我浇花，擦桌子，做饭。
以下是一个基于LSTM的家务机器人设计，该机器人可以执行诸如浇花、擦桌子、做饭等任务。由于篇幅限制，我将提供一个简化的示例代码，展示如何使用LSTM来控制机器人的行为。

基于LSTM的家务机器人设计

import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim

# 定义LSTM模型
class HousekeepingRobotLSTM(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(HousekeepingRobotLSTM, self).__init__()
        self.hidden_size = hidden_size
        self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)
        
    def forward(self, x, hidden=None):
        out, (hidden, cell) = self.lstm(x, hidden)
        out = self.fc(out)
        return out, (hidden, cell)

# 定义任务序列
tasks = [
    "water_flowers",  # 浇花
    "wipe_tables",    # 擦桌子
    "cook_meal"       # 做饭
]

# 将任务转换为数值编码
task_to_idx = {task: idx for idx, task in enumerate(tasks)}
idx_to_task = {idx: task for task, idx in task_to_idx.items()}

# 准备训练数据
# 假设我们有一个简单的任务序列
task_sequence = [
    task_to_idx["water_flowers"],
    task_to_idx["wipe_tables"],
    task_to_idx["cook_meal"]
]

# 转换为输入和目标
input_sequence = task_sequence[:-1]
target_sequence = task_sequence[1:]

# 转换为张量
input_tensor = torch.tensor(input_sequence, dtype=torch.float32).unsqueeze(0).unsqueeze(2)
target_tensor = torch.tensor(target_sequence, dtype=torch.long).unsqueeze(0)

# 初始化模型、损失函数和优化器
input_size = 1
hidden_size = 64
output_size = len(tasks)

model = HousekeepingRobotLSTM(input_size, hidden_size, output_size)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
num_epochs = 1000
for epoch in range(num_epochs):
    outputs, _ = model(input_tensor)
    loss = criterion(outputs.squeeze(1), target_tensor.squeeze(1))
    
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    
    if (epoch+1) % 100 == 0:
        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

# 测试模型
model.eval()
with torch.no_grad():
    test_input = input_tensor[:, :1, :]
    hidden = None
    for i in range(len(tasks)):
        output, hidden = model(test_input, hidden)
        predicted_task_idx = output.argmax(2).item()
        print(f"Predicted task: {idx_to_task[predicted_task_idx]}")
        test_input = torch.tensor([[predicted_task_idx]], dtype=torch.float32).unsqueeze(2)