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[Question] Robot control / Diffusion Policy adaptation #2

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@phamtrongthang123

Hi, thank you for the great work on Generative Modeling via Drifting!

I implemented the drifting model on simulated robot control tasks (PushT, Lift, Can, ToolHang)
by porting the drifting loss into the Diffusion Policy framework: https://github.com/phamtrongthang123/drifting_policy

The loss port passes numerical tests against your JAX implementation. Key config: gen_per_label=8, batch_size=64, dot-product cdist (matching your kernel).

However, some of my results substantially exceed the scores reported in your Table 7, which makes me think there may be a mismatch in my setup that I haven't caught. The most surprising case is ToolHang (low-dim): 0.84 vs paper's 0.38.

Full comparison:

Task Setting Paper (Drifting) My result
Can Visual 0.99 0.98
PushT Visual 0.86 0.86
Lift Visual 1.00 1.00
PushT State 0.86 0.871
Can State 0.98 0.98
Lift State 1.00 1.00
ToolHang State 0.38 0.84 ← large gap

My question: Do you have plans to release an official robot control implementation?
I would not ask you to debug my code because that would be too much. But a release of official code would be very helpful.

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