predict_action function performs pyTorch conversion using GPU

[demobo-com]

What this does

(⚡️ Performance)
Optimizes predict_action function's observations' pyTorch conversions by using CUDA GPU

How to checkout & try? (for the reviewer)

Run the same ACT policy evaluation in these 3 different scenarios:

1. original predict_action without running other CPU intensive applications (ex. Zoom)
2. original predict_action while running other CPU intensive applications (ex. Zoom): very slow fps inferencing
3. new predict_action without running other CPU intensive applications (ex. Zoom): expect a little performance optimization
4. new predict_action while running other CPU intensive applications (ex. Zoom): expect 10x better performance

Examples:

python lerobot/scripts/control_robot.py --control.type=record --control.policy.path=someACT_model --some.option=true

[Copilot]

Pull Request Overview

This PR optimizes predict_action by moving the tensor device transfer to before data transformations so that normalization and reshaping occur on the GPU, improving inference performance under CPU load.

• Moves observation[name].to(device) to the start of the conversion loop and removes the redundant transfer at the end
• Ensures all type casting, normalization, permute, and unsqueeze operations happen on the CUDA device

Comments suppressed due to low confidence (1)

lerobot/common/robot_devices/control_utils.py:112

• Add a unit test to verify that after predict_action, all observation tensors are on the specified device and have the correct shape and dtype.

            observation[name] = observation[name].to(device)

[Copilot]

You can combine device transfer, dtype conversion, and normalization into a single chained call to reduce intermediate allocations, e.g.: observation[name] = observation[name].to(device=device, dtype=torch.float32).div(255).permute(2,0,1).unsqueeze(0).

Suggested change

	observation[name] = observation[name].to(device)
	if "image" in name:
	observation[name] = observation[name].type(torch.float32) / 255
	observation[name] = observation[name].permute(2, 0, 1).contiguous()
	observation[name] = observation[name].unsqueeze(0)
	if "image" in name:
	observation[name] = observation[name].to(device=device, dtype=torch.float32).div(255).permute(2, 0, 1).unsqueeze(0)
	else:
	observation[name] = observation[name].to(device).unsqueeze(0)

[Copilot]

[nitpick] Consider using the .float() alias instead of .type(torch.float32) for readability and consistency with common PyTorch code style.

Suggested change

	observation[name] = observation[name].type(torch.float32) / 255
	observation[name] = observation[name].float() / 255