Prediction with Action: Visual Policy Learning via Joint Denoising Process

English reading report: Method, equations, original figures, experiments and reproducibility.

Authors: Yanjiang Guo; Yucheng Hu; Jianke Zhang; Yen-Jen Wang; Xiaoyu Chen; Chaochao Lu; Jianyu Chen

Affiliations: IIIS, Tsinghua University; Shanghai Qizhi Institute; Shanghai AI Lab; University of California, Berkeley

Source: NeurIPS 2024 · ref-eecf2578168f85db0c45 ↗ · Catalog record

Reading: 436 / 558 · 6 original figures & tables · ~20 min ·

1. Paper overview

In one sentence: PAD couples future-image prediction and robot-pose generation in one denoiser, improving reported manipulation success while increasing inference cost. identity architecture training execution metaworld evaluation-scope author-limitations

At a glance	What to know
Research problem	Source description Limited robot demonstrations constrain visual imitation learning. PAD asks whether future-image supervision and action-free video training can improve control when prediction and action share a model. The authors motivate this through shared physical dynamics; their experiments test task success, not a general proof of physical understanding. problem joint image-quality
Core mechanism	Source description One DiT jointly predicts future RGB and robot poses, with optional depth. Shared attention couples modalities inside the denoiser. architecture depth
A key reported result	Reported MetaWorld multi-task success: PAD 72.5% Reported average success rate. One text-conditioned policy; 50 demonstrations per task; corner2 RGB and pose, without depth. Appendix A.4 evaluates 48 listed tasks with 25 rollouts each and excludes two handle-pull tasks. GR-1 57.4%; a calculated 15.1 percentage-point gain, approximately 26.3% relative. Strong reported aggregate improvement. The main table calls this an all-50-task average, but the exclusions prevent an unqualified full-benchmark claim. No seed variation or confidence interval is reported. data metaworld evaluation-scope
Reading caution	Author claim The authors identify low control frequency and testing only RGB, pose and depth. No measured control rate is supplied; tactile and point-cloud extensions remain untested. author-limitations depth

Core contributions

Source description
One DiT jointly predicts future RGB and robot poses, with optional depth. Shared attention couples modalities inside the denoiser. architecture depth
Source description
Padding and attention masks permit training with missing action/depth modalities, enabling RGB-only video data to contribute to the same backbone. architecture
Reader analysis
Ablations support the usefulness of image prediction and video training, although they do not isolate image accuracy as the causal mediator of control improvements. metaworld ablation

PAD architecture with RGB, robot-pose and optional extra-modality columns passing upward through a shared diffusion transformer; an expanded block shows masked attention and frozen CLIP conditioning. — **Figure 3.** Current observations condition one shared denoiser for future images and robot poses. Original paper, p. 4 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.

2. Motivation

2.1 The problem and the proposed response

Source description

Limited robot demonstrations constrain visual imitation learning. PAD asks whether future-image supervision and action-free video training can improve control when prediction and action share a model. The authors motivate this through shared physical dynamics; their experiments test task success, not a general proof of physical understanding. problem joint image-quality

2.2 What this reading follows

A robot policy can learn from demonstrations that contain actions, while a video model can learn from images alone. PAD brings those two learning signals into one diffusion transformer: current observations and language condition the joint generation of future image latents and desired robot poses. Its experiments compare control success, image-prediction ablations, video training and compute allocation. This reading follows the information flow before interpreting those comparisons. It also preserves two important boundaries: the concrete training schedule is staged, and the appendix excludes two MetaWorld tasks despite the main table's all-50 wording. The observed source is the November 2024 arXiv v1. identity architecture training execution metaworld evaluation-scope author-limitations

3. Research context

We place the paper in the collection through its world–action interface. The catalog labels and the reading’s assessment are shown separately.

Catalog dimension	Recorded classification
Major category	WAMs
Architecture	One Model
Prediction paradigm	Joint prediction
Quadrant	Q1 · One Model × Joint prediction

3.1 Evidence-based assessment

Supports the recorded classification

Reader analysis

The recorded One Model × Joint prediction classification is supported by shared DiT attention over image/action tokens and simultaneous denoising at inference. Modality encoders and decoders do not constitute a separate predictive model followed by a controller. The mechanism is joint future/action sampling, not inverse dynamics; closed-loop execution is also distinct from reward-scored planning. architecture joint execution

This is the collection’s architectural analysis, not a new related-work survey. Benchmark comparisons and their protocols appear in Section 6.

4. Problem formulation

4.1 Inputs and outputs

Inputs	Outputs
Current RGB image and robot pose Natural-language instruction Optional current depth map	Jointly sampled future RGB images and desired robot poses over k steps Future depth maps when the depth modality is enabled

4.2 Equations and their role

L_I=[\varepsilon_I(c_I),z_t^I]

Section 3.2 concatenates the encoded current RGB observation c_I with noisy future RGB latent z_t^I at diffusion step t. The brackets mean channel concatenation. Analogous constructions serve action and extra modalities; only future targets receive diffusion noise. architecture objective

\begin{aligned}\mathcal{L}_{\mathrm{diff}}^\delta(\theta)&=\mathbb{E}_{\epsilon^\delta\sim\mathcal{N}(0,1),t,C,l}\!\left[\left\|\epsilon^\delta-\epsilon_\theta^\delta(z_t^\delta,t,C,l)\right\|_2^2\right],\quad \delta\in\{I,A,E\},\\\mathcal{L}(\theta)&=\lambda_I\mathcal{L}_{\mathrm{diff}}^I+\lambda_A\mathcal{L}_{\mathrm{diff}}^A+\lambda_E\mathcal{L}_{\mathrm{diff}}^E.\end{aligned}

Equations (3)–(4): I, A and E index RGB, action and extra modalities; epsilon is sampled noise, epsilon_theta its learned estimate, z_t the noisy latent, C current observations, l the instruction and theta model parameters. The lambda weights balance the modality losses. objective

5. Method in detail

5.1 Build one joint prediction from the current observation

Source description

Begin with the current RGB image, current robot pose and instruction, adding depth only for the depth-enabled model. PAD encodes RGB with a frozen VAE and language with frozen CLIP; robot poses pass through an MLP. Within each modality, it stacks the current latent and noisy future latents along channels before creating tokens. This detail matters: three future frames do not simply mean three independent sets of spatial tokens. The current pose and future targets are likewise packed into one action token in the appendix's implementation. Shared DiT attention then lets image, action and optional depth representations interact during denoising. RGB-only video examples use padding masks for the missing modalities. The diagram's upward flow and masked-attention block agree with Section 3.2's joint construction. architecture joint implementation

5.2 Separate learning the future from executing a pose

Source description

Training starts from ImageNet-pretrained DiT weights, followed by BridgeData-v2 video pretraining and robot-domain adaptation. Equations (3)–(4) train the network to remove injected noise from available future targets; the loss is not a reward for executing an action. The RGB loss coefficient stays fixed while action and depth coefficients increase during adaptation. At deployment, PAD begins future latents from noise and uses 75 DDIM steps to predict three futures separated by a frame interval of four. It executes only the first desired pose through linear interpolation, then observes and predicts again. This is the paper's closed-loop feedback mechanism. Generated images help define the joint sample, while the physical robot moves according to the pose output; image generation alone is not a completed manipulation. initialization objective training execution joint

5.3 Interpret the gains without overstating the mechanism

Reader analysis

Reader analysis: the MetaWorld table supports a useful design choice, but not every proposed explanation for it. Removing image prediction lowers the reported average from 72.5% to 43.6%, and removing video co-training lowers it to 59.2%. Those changes are consistent with richer supervision and transferable prediction features helping control. They do not isolate whether sharper images themselves cause better actions, because supervision, data exposure and learned representations change together. The depth comparison similarly adds both a sensor and a prediction target. Read the quantitative evidence alongside the appendix: two MetaWorld tasks are excluded, some task cells conflict, and uncertainty across training seeds is not reported. The qualitative futures reveal errors as well as plausible predictions, so successful task execution is the stronger evidence of practical value. metaworld ablation panda depth evaluation-scope table-discrepancies image-quality

5.4 Training and inference

During training

Source description

Start from ImageNet-pretrained DiT weights. Remove class embeddings, zero-initialize new text and robot-state layers, and replicate the image-tokenizer weights for the current/future stack. initialization

Source description

Pretrain for 200,000 steps on BridgeData-v2, described as 60,000 trajectories, then adapt for 100,000 steps per robot domain. The paper reports about two days plus one day on four NVIDIA A100 GPUs. It does not specify a video/robot mixing ratio during adaptation. training

Source description

Train by modality-weighted noise prediction. Keep the RGB coefficient at 1 and linearly raise action/depth coefficients from 0 to 2 during adaptation. Appendix settings include learning rate 1e-4, batch size 256 and 256-by-256 RGB input. objective training implementation

During inference

Source description

Use k=3 future predictions spaced by frame interval i=4 and 75 DDIM denoising steps. Decode the first desired pose, move toward it with linear interpolation, then obtain a fresh observation. Future images participate in inference; a separate goal-image policy or inverse-dynamics model is not specified. joint execution architecture

5.5 Implementation flow

Encode current observations and future targets
A frozen VAE encodes RGB; an MLP encodes robot poses; a frozen CLIP encoder supplies language conditioning. Depth is downsampled. Actions represent desired end-effector position, rotation where applicable, and gripper status. joint architecture data
Stack time, then combine modalities
Current and noisy future latents are concatenated along channels within each modality. Patchification creates image tokens; the pose sequence becomes one token. XL/2 uses 256 RGB tokens; optional depth adds 16. All available tokens enter shared DiT attention. architecture implementation
Handle missing supervision
RGB-only examples contain no action targets. PAD pads absent modalities, excludes padding through the attention mask and discards padded outputs. The architecture supports heterogeneous data; the reported experiment uses a staged pretraining/adaptation schedule. architecture training

6. Experiments & results

PAD learns a language-conditioned robot policy by jointly denoising future image latents and robot poses in one diffusion transformer. RGB video training supplies prediction experience without requiring action labels. The robot executes the first predicted pose and observes again. Reported control gains are substantial, but the MetaWorld headline needs qualification because the appendix excludes two tasks.

6.1 Read the original evidence

MetaWorld success table with easier and harder task subsets, six policies, two PAD ablations and a green aggregate column reporting PAD 0.725. — **Table 1.** The reported aggregate favors PAD, with large drops when image prediction or video training is removed. Original paper, p. 6 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.

Panda seen-task success table comparing four baselines, PAD and PAD-Depth over button, cable, pick, place and drawer tasks; averages are 0.72 for PAD and 0.78 with depth. — **Table 2.** Depth improves the reported average on executed Panda manipulation tasks. Original paper, p. 7 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.

Expert-data and easy, middle and hard Panda scenes with yellow target boxes beside a success-rate bar chart; PAD has the tallest bar in each tier. — **Figure 5.** PAD leads the plotted comparisons as clutter, objects and backgrounds change. Original paper, p. 7 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.

6.2 Results and evaluation conditions

Task & protocol	Reported result	Comparison & interpretation
Reported MetaWorld multi-task success One text-conditioned policy; 50 demonstrations per task; corner2 RGB and pose, without depth. Appendix A.4 evaluates 48 listed tasks with 25 rollouts each and excludes two handle-pull tasks.	PAD 72.5% Reported average success rate	GR-1 57.4%; a calculated 15.1 percentage-point gain, approximately 26.3% relative. Strong reported aggregate improvement. The main table calls this an all-50-task average, but the exclusions prevent an unqualified full-benchmark claim. No seed variation or confidence interval is reported. data metaworld evaluation-scope
MetaWorld image-prediction and video-training ablations Same reported MetaWorld evaluation; two handle-pull tasks excluded in the detailed table.	Full PAD 72.5%; without image prediction 43.6%; without video co-training 59.2%. Reported average success rate	Calculated drops of 28.9 and 13.3 percentage points. Both components help in this setup. Removing supervision or pretraining changes more than image sharpness alone. metaworld ablation evaluation-scope
Panda in-distribution manipulation 200 demonstrations per task; 50 evaluation rollouts per task. Pick and place columns aggregate four and three tasks.	PAD 72%; PAD-Depth 78%. Reported average success rate	RT-2* 69%; depth adds 6 percentage points over PAD. These are executed physical manipulation results. Retain the reported average rather than equally averaging the six displayed category cells. RT-2* is a reimplementation; PAD does not lead every category. data panda baselines expert-tasks
Panda generalization across distractors, objects and backgrounds Three difficulty tiers with depth excluded; hard settings introduce unseen objects or backgrounds.	PAD has the highest plotted success at every tier. Success-rate ordering in Figure 5	Compared with Diffusion Policy, SuSIE, RT-1 and RT-2*. A qualitative ordering is supported. Exact tier values and evaluation counts are not tabulated, so the abstract's 28.0% improvement is not converted into a precise relative or percentage-point claim. identity generalization expert-tasks
MetaWorld compute and patch-size scaling Table 3 model variants; Appendix A.5 again excludes the two handle-pull tasks.	XL/2: 72.5%, 119.1 GFLOPs; XL/4: 64.5%, 29.5; XL/8: 48.2%, 7.7. Reported average success and transformer GFLOPs	All three report 661M parameters; smaller patches increase token count. The observed relationship supports a compute/accuracy tradeoff. It does not establish a universal scaling law or measure real-time control latency. scaling scaling-appendix

6.3 Ablations and diagnostic examples

Read component removals and qualitative examples within their stated evaluation conditions.

Two prediction examples, one simulated and one tabletop, each with a condition image and ground-truth, with-video and without-video future sequences arranged in three rows. — **Figure 8.** Selected future predictions illustrate the video-training ablation. Original paper, p. 9 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.

PAD scaling table comparing XL/2, XL/4, XL/8, L/2 and B/2 by layers, width, attention heads, tokens, parameters, GFLOPs and average success. — **Table 3.** Patch size changes compute and success even at the same reported parameter count. Original paper, p. 10 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.

7. Analysis & limitations

7.1 What the evidence leaves open

Author claim

The authors identify low control frequency and testing only RGB, pose and depth. No measured control rate is supplied; tactile and point-cloud extensions remain untested. author-limitations depth

Reader analysis

Qualitative futures can differ in object identity or timing. The paper's physical-knowledge explanation exceeds what selected images and task-success ablations alone establish. image-quality ablation

Reader analysis

Table cells conflict: GR-1 button-press-topdown is 0.84 in Table 1 versus 1.00 in Table 5; PAD handle-press is 0.80 in Table 5 versus 0.96 in Table 6. Reported aggregates are preserved, not silently repaired. Two omitted tasks, absent seed uncertainty and differing pretrained baseline histories limit precision and comparability. table-discrepancies evaluation-scope baselines

7.2 Questions for discussion

Would blocking image–action attention only during inference separate the value of shared training from ongoing joint denoising? [architecture; ablation]
How much success survives a lower DDIM-step budget when both wall-clock latency and identical rollout conditions are measured? [execution; author-limitations]

8. Reproducibility audit

8.1 Requirements and known gaps

Source description

A faithful attempt requires the specified pretrained DiT/VAE/CLIP components, Bridge video data, domain demonstrations, camera/pose interfaces and the appendix's exact instruction mapping. Preserve unusual mappings such as push-v2 to 'pick red object' until clarified. initialization architecture data training instructions

Reader analysis

Clarify pose normalization, optimizer details, diffusion scheduling, adaptation data mixing, evaluation seeds and task-success checks before attempting exact replication. The supplied implementation section gives shapes and learning rate but not a complete runnable configuration. Proposed checks should separately test training supervision and inference-time coupling. implementation training execution evaluation-scope

8.2 Proposed reproduction checks

The following checks are proposals motivated by the paper. They have not been run as part of this reading.

Check 1: Test future-image supervision with the architecture held fixed

Reader-proposed check, not performed: initialize paired models from the same video-pretrained checkpoint and adapt them on identical robot demonstrations. Keep the DiT, current RGB/pose conditioning, future-image token slots, action-loss schedule, update count and inference sampler fixed; change only whether the future-RGB denoising loss contributes during adaptation. Evaluate the appendix's included MetaWorld tasks on matched initial states with multiple training seeds, keeping the two excluded tasks separate. Report action success and held-out future-image error independently. If image supervision consistently raises success under this controlled comparison, it supports a benefit beyond an architectural change. Improved image scores without a success gain would weaken the proposed link. This is a targeted control, not an exact recreation of the paper's underspecified no-image variant. architecture training objective metaworld ablation evaluation-scope

Check 2: Measure the denoising-speed versus control-success tradeoff

Reader-proposed check, not performed: hold a trained PAD checkpoint, prediction horizon, frame interval, interpolation controller and evaluation initial states fixed, then compare the reported 75-step DDIM sampler with shorter sampling budgets. Record complete observation-to-pose latency on the same hardware, actual control-update frequency, success per task and prediction failures. Run paired seeds rather than interpreting transformer GFLOPs as latency. A shorter sampler that preserves success while reducing latency would support a practical remedy for the authors' stated bottleneck. A sharp loss of success would expose how strongly the policy depends on extensive joint denoising. Document the exact timestep schedule and hardware, which the supplied material does not fully determine for this comparison. execution author-limitations scaling implementation evaluation-scope

8.3 Reading coverage

Visual audit: The title/author/version page, all method and experiment pages, and all appendix pages were rendered and visually inspected. This includes Figures 1–12 and Tables 1–7, the training hardware/schedule on page 6, implementation shapes and settings on page 15, task exclusions on pages 17–18, and instructions on page 19. Each of the six final original crops was also inspected for legibility, full labels and faithful boundaries. Figure 3's arrow direction, missing-modality mask and frozen CLIP marker were checked against Sections 3.1–3.3; its transformer-block repetition was distinguished from DDIM sampling. Tables 1, 5 and 6 were cross-checked and unresolved discrepancies retained. All six text chunks, including references on pages 11–14, were read; those reference pages were not visually rendered. Separate supplements, linked videos and code remain outside this reading.

PDF pages inspected for this edition: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 16, 17, 18, 19. Appendix coverage: reviewed.

Original figures and tables remain the work of the source’s authors. Extractions preserve their scientific content; any HTML wrapper layout is disclosed with each figure. The surrounding reading notes are our own.

Text reading scope & known omissions

Abstract
1 Introduction
2 Preliminaries
3 PAD: Prediction with Action via Joint Denoising Process
3.1 Overview of PAD
3.2 Model Architectures
3.3 Training Process
4 Experiments
4.1 Environmental Setups and Baselines
4.2 Main Results
4.3 Generalization Analysis
4.4 Ablation Studies
4.5 Scaling Analysis
5 Related Work
6 Conclusion and Discussion
References
A Appendix
A.1 Additional Implementation Details of PAD
A.1.1 Input Encoder and Output Decoders
A.2 Additional Implementation Details of Baselines
A.2.1 Additional Model Training Details
A.3 Real world Experiment Details
A.4 Details Baselines and Ablations in Metaworld
A.5 Detailed Scaling Results
A.6 Instructions used in tasks

Outside the original text pass

Text extraction does not reconstruct figure images; inspect the retained PDF for figures and equation/table layout.
Version scope: arXiv:2411.18179v1, 27 November 2024, identified on the inspected title page; the exact title and all seven catalog authors match. The PDF carries a NeurIPS 2024 footer. A separate proceedings edition and later revisions were not supplied or compared.
All six supplied text chunks were read individually, covering PDF pages 1–19, including references and Appendix A.1–A.6. Reference pages 11–14 were read as text only; all other pages were also visually inspected.
Text extraction does not reconstruct figure images; this omission was addressed by rendering the retained PDF and inspecting its figures and tables.
Separate supplemental material availability has not been fully verified.
Linked project videos, external papers and code repositories were not inspected. No installation or experiment was performed.

The visual audit above records the subsequent illustrated pass.

8.4 Traceable evidence

identityPDF p. 1, title block, arXiv margin, conference footer and AbstractInspect

The exact title and seven authors match the catalog. The artifact is arXiv:2411.18179v1 [cs.RO], dated 27 November 2024, with a NeurIPS 2024 footer. Its abstract states an unseen-task improvement of 28.0% without defining that comparison's denominator there.

1. Paper overview

Core contributions

2. Motivation

2.1 The problem and the proposed response

2.2 What this reading follows

3. Research context

3.1 Evidence-based assessment

4. Problem formulation

4.1 Inputs and outputs

4.2 Equations and their role

5. Method in detail

5.1 Build one joint prediction from the current observation

5.2 Separate learning the future from executing a pose

5.3 Interpret the gains without overstating the mechanism

5.4 Training and inference

During training

During inference

5.5 Implementation flow

6. Experiments & results

6.1 Read the original evidence

6.2 Results and evaluation conditions

6.3 Ablations and diagnostic examples

7. Analysis & limitations

7.1 What the evidence leaves open

7.2 Questions for discussion

8. Reproducibility audit

8.1 Requirements and known gaps

8.2 Proposed reproduction checks

Check 1: Test future-image supervision with the architecture held fixed

Check 2: Measure the denoising-speed versus control-success tradeoff

8.3 Reading coverage

Outside the original text pass

8.4 Traceable evidence

8.5 Primary sources

Prediction with Action: Visual Policy Learning via Joint Denoising Process ↗