Learning From Corrupt/Noisy Data

July 1, 2025

Adam Wei

Agenda

1. Review of Ambient Diffusion
2. Experiments & Results
3. [Time Permitting]: Next directions

Part 1

Ambient-Omni

Corrupt Data

Corrupt Data (\(q_0\))

Clean Data (\(p_0\))

Sources of Robot Data

"corrupt"

"clean"

Research Questions

1. How can we learn from both clean and corrupt data?
2. Can these algorithms be adapted for robotics?

Research Questions

Giannis Daras

Ambient Diffusion

Three key ideas

1. Learning in the high-noise regime

\(p_0\)

\(q_0\)

1. Learning in the high-noise regime

\(\exists \sigma_{min}\) s.t. \(d_\mathrm{TV}(p_{\sigma_{min}}, q_{\sigma_{min}}) < \epsilon\)

• Only use samples from \(q_0\) to train denoisers with \(\sigma > \sigma_{min}\)
• Corruption level of each datapoint determines its utility

\(\sigma=1\)

\(\sigma=0\)

\(\sigma=\sigma_{min}\)

Contraction as masking

• Contracting \(q_{\sigma_{min}}\) towards \(p_{\sigma_{min}}\) "masks" corruption between \(p_0\) and \(q_0\)

Contraction introduces bias

• \(p_0\) and \(q_0\) are reasonably similar
• Number of samples from \(q_0\) is large compared to \(p_0\)
• ... other mild conditions

2. Ambient Loss

\(\sigma=1\)

\(\sigma=0\)

\(\sigma=\sigma_{min}\)

Clean Data

Corrupt Data

Without Ambient Loss

\(\sigma=1\)

\(\sigma=0\)

\(\sigma=\sigma_{min}\)

\(\mathbb E[\lVert h_\theta(x_t, t) - x_0 \rVert_2^2]\)

\(\mathbb E[\lVert h_\theta(y_t, t) - y_0 \rVert_2^2]\)

Clean Data: \(x_0\)-prediction or \(\epsilon\)-prediction

Corrupt Data: \(x_0\) or \(\epsilon\)-prediction

\( x_0 \sim p_0\)

\( y_0 \sim q_0\)

Ambient Loss

\(\sigma=1\)

\(\sigma=0\)

\(\sigma=\sigma_{min}\)

\(\mathbb E[\lVert h_\theta(x_t, t) - x_0 \rVert_2^2]\)

\(\mathbb E[\lVert h_\theta(y_t, t) + \frac{\sigma_{min}^2\sqrt{1-\sigma_{t}^2}}{\sigma_t^2-\sigma_{min}^2}y_{t} - \frac{\sigma_{t}^2\sqrt{1-\sigma_{min}^2}}{\sigma_t^2-\sigma_{min}^2} y_{t_{min}} \rVert_2^2]\)

Corrupt Data: ambient loss*

\( x_0 \sim p_0\)

\( y_0 \sim q_0\)

* Giannis and I are working on the \(\epsilon\)-prediction versions

Clean Data: \(x_0\)-prediction or \(\epsilon\)-prediction

Ambient Loss

\(\sigma=1\)

\(\sigma=0\)

\(\mathbb E[\lVert h_\theta(x_t, t) - x_0 \rVert_2^2]\)

Corrupt Data: ambient loss*

\( x_0 \sim p_0\)

\( y_0 \sim q_0\)

* Giannis and I are working on the \(\epsilon\)-prediction versions

\(\sigma=\sigma_{min}\)

\(\sigma_{buffer}(\sigma_{min}, \sigma_t)\)

\(\mathbb E[\lVert h_\theta(y_t, t) + \frac{\sigma_{min}^2\sqrt{1-\sigma_{t}^2}}{\sigma_t^2-\sigma_{min}^2}y_{t} - \frac{\sigma_{t}^2\sqrt{1-\sigma_{min}^2}}{\sigma_t^2-\sigma_{min}^2} y_{t_{min}} \rVert_2^2]\)

Clean Data: \(x_0\)-prediction or \(\epsilon\)-prediction

Training Implementation Details

\(\sigma=1\)

\(\sigma=0\)

\(\mathbb E[\lVert h_\theta(x_t, t) - x_0 \rVert_2^2]\)

\( x_0 \sim p_0\)

\( y_0 \sim q_0\)

\(\sigma=\sigma_{min}\)

\(\sigma_{buffer}(\sigma_{min}, \sigma_t)\)

\(\mathbb E[\lVert h_\theta(y_t, t) + \frac{\sigma_{min}^2\sqrt{1-\sigma_{t}^2}}{\sigma_t^2-\sigma_{min}^2}y_{t} - \frac{\sigma_{t}^2\sqrt{1-\sigma_{min}^2}}{\sigma_t^2-\sigma_{min}^2} y_{t_{min}} \rVert_2^2]\)

• Dataloader: sample noise uniformly, then sample a datapoint
• Some other details... see paper

3. Learning in the low-noise regime

Ambient-o also presents a way to use OOD data to train denoisers in the low-noise regime

• Ex. Training a dog generator with images of cats

Part 2

Experiments

Three key ideas -- tried two so far...

Experiments

1. Corrupt data points should be contracted (masked with noise) and used at higher noise levels

2. Ambient loss function

3. OOD data with locality can be used at lower noise levels

Experiments

• cotraining with no reweighting
• setting \(\sigma_{min} = 0\)

\(\sigma_{min}\)

• Find \(\sigma_{min}\) by training a classifier
• Can be assigned \(\sigma_{min}\) per-dataset or per-datapoint

Datasets and Task

"Clean" Data

\(|\mathcal{D}_T|=50\)

Results

Experiments

Choosing \(\sigma_{min}\): Swept several values on the dataset level

Loss function: Tried all 4 combinations of

{\(x_0\)-prediction, \(\epsilon\)-prediction} x {denoising, ambient}

Preliminary Observations

(will present best results on next slide)

• \(\epsilon\)-prediction > \(x_0\)-prediction and denosing > ambient loss
• Small (but non-zero) \(\sigma_{min}\) performed best
• Ambient diffusion scales slightly better with more sim data than cotraining

Results

\(|\mathcal{D}_S| = 50\), \(|\mathcal{D}_S| = 2000\), \(\epsilon\)-prediction with denoising loss

Results: Success Rate vs \(\sigma_{min}\)

Results: Success Rate vs \(\sigma_{min}\)

Results: Success Rate vs \(\sigma_{min}\)

Results: Success Rate vs \(\sigma_{min}\)

Summary

• \(\epsilon\)-prediction > \(x_0\)-prediction

• "Best" policy used contraction (i.e. \(\sigma > \sigma_{min}\)), but no ambient loss

• Best \(\sigma_{min}\) was the low, but not zero
  • In this set up... sim data is only slightly corrupt; high quality

• "Best" policy attains 85.5% success rate. Best baseline policy attains 84.5% success rate
  • This is not statistically significant...

Discussion

This is an unfavorable setting for ambient diffusion

• Sim data is barely corrupt \(\implies\) effect of ambient diffusion is reduced
• Hyperparameters, sim, etc tuned for baseline

1. Per-datapoint \(\sigma_{min}\)
2. Scaling \(|\mathcal{D}_S|\) (cotraining plateau?)
3. Cropping and locality ideas from ambient-o
4. Tuned hyperparameters

Part 3

Next Directions

Next Directions

• High-noise regime: planning, decision-making
• Low-noise regime: traj opt, control, interpolation, etc

• High-noise regime: contraction + ambient loss
• Low-noise regime: locality

Corruptions in Robotics

\(q_0\) Trajectory Quality

\(q_0\) Planning Quality

Experiment 1:

RRT vs GCS

Low

Medium

Corruption Regime

High-frequencies

Experiment 1: RRT vs GCS

GCS

(clean)

RRT

(clean)

Task: Cotrain on GCS and RRT data

Goal: Sample clean and smooth GCS plans

• Example of high frequency corruption
• Common in robotics
  • Low quality teleoperation or data gen

Corruptions in Robotics

\(q_0\) Trajectory Quality

\(q_0\) Planning Quality

Experiment 1:

RRT vs GCS

Low

Medium

Corruption Regime

High-frequencies

Experiment 2:

Cross-embodiment (Lu)

Low

Medium

Low-fequencies

Experiment 2: Lu's Data

• Same task + plan structure, different robot embodiment
  • Plans are high quality
  • Trajectories (EE-space) have embodiment gap (lower quality)

Corruptions in Robotics

\(q_0\) Trajectory Quality

\(q_0\) Planning Quality

Experiment 1:

RRT vs GCS

Experiment 3:

Bin Organization

Low

Medium

Medium

Incorrect

Corruption Regime

High-frequencies

Low-fequencies

Experiment 2:

Cross-embodiment (Lu)

Low

Medium

High-fequencies

Experiment 3

Task: Pick-and-place objects into specific bins

• Motions may be reasonable and informative
• But the decision making is incorrect

Locality

In image generation...

• At low noise-levels, denoising can be performed near-optimally using local patches
• Let crop(t) be the crop size required to diffuse optimally at time t
• Let \(A_p(t)\) and \(A_q(t)\) be random crops of images from p and q of size crop(t)

• If the distribution of \(A_p(t)\) and \(A_q(t)\) are similar, then images from q can be used to train a denoiser for p for noise levels < t

Locality

Cat images can be used to train a generative models for dogs!