VILP: Imitation Learning with Latent Video Planning
1. Paper overview
In one sentence: VILP makes video-guided imitation learning faster by generating compressed future frames and translating them into actions with a separate policy, while retaining task-specific data and control limitations. e02-probleme03-compressione05-conditioninge06-policye07-protocole10-datae11-scarce-resultse16-real
| At a glance | What to know |
|---|---|
| Research problem | Source description Video plans can convey task intent without action labels, but slow generation prevents frequent replanning. Turning imagined frames into executable actions and conditioning consistently on multiple cameras are separate challenges. VILP addresses these within task-specific imitation learning. e02-probleme05-conditioninge06-policy |
| Core mechanism | Source description A frozen image codec and conditioned 3D U-Net move video diffusion into latent space; distinct per-view generators share information from all observed views. e03-compressione04-diffusione05-conditioning |
| A key reported result | Arrange-Blocks-Hybrid: VILP-8: 84.0 ± 0.0 / 77.6 ± 2.8 Max / selected five-rollout mean success rate (%), mean ± standard deviation across seeds. Simulation; 610 planner videos and 76 labeled demonstrations. Three seeds, each evaluated on 50 environments; rollouts every 100 training epochs. Diffusion Policy: 22.7 ± 3.1 / 17.1 ± 1.2; UniPi-16: 18.7 ± 2.3 / 16.2 ± 1.7. Both statistics select performance during training; they are not fixed-final-checkpoint estimates. Extra video access distinguishes VILP from Diffusion Policy. e10-datae11-scarce-results |
| Reading caution | Reader analysis Figure 7's green action-horizon curve is labeled video horizon 9, while p. 7 recommends video/action horizons 6/8. The plot does not directly verify that recommendation; the discrepancy remains unresolved. Neither plot shows uncertainty. e15-horizon |
Core contributions
- Source description
A frozen image codec and conditioned 3D U-Net move video diffusion into latent space; distinct per-view generators share information from all observed views. e03-compressione04-diffusione05-conditioning
- Reader analysis
Separate video and action training enables useful transfer from off-target action demonstrations. This is demonstrated under specific Small/Hybrid data configurations, rather than universal video-to-action transfer. e10-datae11-scarce-resultse17-future
Figure 2. The planner predicts a compact video before reconstructing its frames. Original paper, p. 3 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. Start with panel (a): the image encoder produces a latent with spatial dimensions H and W and channel count C; the decoder reconstructs the image. In panel (b), the current multiview observation o_t conditions a stack of N predicted future latents, which the same decoder turns into frames sampled at interval Δt. Panel (c) expands the denoiser: noisy latents enter from below, and the return arrow indicates repeated denoising. The visual encoders supply c_t to the attention blocks. Section IV-D identifies U-Net features as queries and observation embeddings as keys and values; these conditioning encoders are distinct from the frozen image-compression encoder. e03-compressione04-diffusione05-conditioninge06-policy
What it supports. The diffusion model spends its iterations on compressed video representations, and observation information enters throughout the U-Net. This is the architectural basis for the paper's efficiency argument. The figure ends with predicted frames: the separate action mapper and the robot feedback loop appear in Figure 3 and Section V.
Where the evidence stops. The drawing summarizes a planner, not a single network that jointly emits video and actions. Sections IV-D and V specify separate per-view generators and an action mapper. Shared observation conditioning alone does not mathematically guarantee synchronized generated views.
2. Motivation
2.1 The problem and the proposed response
Video plans can convey task intent without action labels, but slow generation prevents frequent replanning. Turning imagined frames into executable actions and conditioning consistently on multiple cameras are separate challenges. VILP addresses these within task-specific imitation learning. e02-probleme05-conditioninge06-policy
2.2 What this reading follows
VILP asks whether a robot can learn what should happen from videos and learn how to move from a smaller, imperfectly matched action dataset. Its planner generates future images in latent space, while a separate frame-pair policy supplies actions and fresh observations close the loop. This division is central to both the speed results and the data argument. The evidence spans video-quality benchmarks, simulated execution, module and horizon ablations, and one physical task. Read those layers separately: favorable generated-video metrics do not establish execution success, and the largest simulation gains use additional task videos unavailable to the direct-action baseline. e02-probleme03-compressione05-conditioninge06-policye07-protocole10-datae11-scarce-resultse16-real
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 | Not assigned |
| Architecture | Not assigned |
| Prediction paradigm | Not assigned |
| Quadrant | Not assigned |
This table preserves the labels recorded at reading time. The current major category is WAMs. View the current classification.
3.1 Evidence-based assessment
Insufficient evidence to decide
The recorded catalog has no assignment to affirm or contradict. Architecture evidence supports a modular video world model plus a separate inverse-dynamics-style action mapper, used during inference with observation feedback. Future video generation and action prediction are sequential, not a joint future/action output from one model. This assessment leaves the catalog unchanged. e05-conditioninge06-policye10-data
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 |
|---|---|
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4.2 Equations and their role
5. Method in detail
5.1 Separate the representation problem from the control problem
The first learning stage decides how to represent a frame, not which action to execute. VQGAN learns image compression with perceptual and adversarial objectives, then remains frozen while the planner learns a distribution over future latent stacks. The planner's training target is injected noise, conditioned on the current observation. At inference, DDIM reverses that process and the decoder returns images. Only then does the low-level policy infer actions from pairs of frames. This separation explains how the video model can train on actionless task episodes while the action mapper uses labeled demonstrations. Reader interpretation: the potential data benefit comes from allocating different supervision to different components; it does not establish that any arbitrary internet video can supervise the robot's executable action space. e03-compressione04-diffusione05-conditioninge06-policye10-datae17-future
5.2 Follow both the camera information and the feedback loop
Multiple cameras enter the planner through separate observation encoders whose embeddings are concatenated. Every per-view diffusion model receives that combined condition, rather than seeing only the camera it predicts. Cross-attention injects the condition into intermediate U-Net features. After decoding, a reused CNN/MLP mapper reads adjacent observations and predicts actions for the interval between them. Figure 3 anchors the sequence with the actual current observation, so the first segment connects the present to a predicted future. The robot executes only an initial prefix and supplies fresh observations for another plan. Three counts therefore have different meanings: DDIM iterations govern generation effort, video horizon governs imagined frames, and executed action horizon governs when observation feedback arrives. The experiments vary these controls separately, with the Figure 7 legend/prose discrepancy preserved. e05-conditioninge06-policye07-protocole12-denoisinge15-horizon
5.3 Read the empirical argument as three different tests
Table I first asks whether generated clips resemble held-out task videos while being fast enough to sample frequently. It does not execute those plans. Table III then tests whether planners pretrained on task videos help when action labels are scarce or include off-target demonstrations; its headline rates select checkpoints during training. Table V changes the regime to fully action-labeled data and averages the last ten checkpoints, so its percentages answer a different comparison. Finally, Table VII executes a physical task and distinguishes complete success from one-block-only failure. Reader interpretation: the paper's strongest supported argument combines computational efficiency with useful modular supervision. Claims of a universal policy or a generally accurate dynamics simulator go beyond these task-specific comparisons and the future-work scope the authors themselves describe. e07-protocole08-video-resultse10-datae11-scarce-resultse13-full-labele16-reale17-future
5.4 Training and inference
During training
Train the VQGAN using perceptual and patch-based adversarial objectives, then freeze it during diffusion training. Train the conditioned 3D U-Net by predicting injected noise. The action mapper is learned from action-labeled data separately; its exact supervised loss is not specified in the PDF. e03-compressione04-diffusione06-policye10-data
Arrange-Blocks and Nut-Assembly planners use 610 and 200 video episodes respectively. Nut Small has 10 labeled demonstrations; Hybrid adds 75 with different goals and 75 with different objects. Arrange Small has 16 gray-block demonstrations; Hybrid adds 60 purple-block demonstrations. Diffusion Policy receives action-labeled data only. e10-data
During inference
Sample latent noise, run DDIM conditioned on current observations, decode each view's future frames, and map adjacent frames to actions. Execute only the first N_e actions, acquire fresh observations, and repeat. Model suffixes such as VILP-4 denote DDIM denoising steps, not executed action steps. e04-diffusione06-policye07-protocol
5.5 Implementation flow
- Compress future frames
VQGAN maps an image f to a spatial latent z and reconstructs it with a decoder. Future frames sampled N times at interval Δt become a temporal stack of latents. This retains spatial structure while shrinking the diffusion domain. e03-compressione04-diffusion
- Condition and denoise
Separate, non-pretrained modified ResNet-18 encoders process observed views; depth is repeated into three channels. Their concatenated embeddings form c_t. Cross-attention supplies keys and values from c_t and queries from flattened temporal/spatial U-Net features. Each view has its own diffusion model trained from scratch. e05-conditioning
- Convert a plan into control
The low-level module uses two CNN encoders and an MLP to map adjacent observation frames to the intervening action segment. It reuses the same mapper across the plan. Figure 3 shows the actual current observation anchoring the first pair. e06-policy
6. Experiments & results
VILP learns observation-conditioned future videos in a compressed latent space, decodes them, and maps adjacent frames to actions through a separate low-level policy. This makes short-horizon video planning practical on the tested tasks and lets task videos supply information beyond scarce action labels. Simulation gains depend on data and evaluation protocol; real-robot evidence comprises 15 trials per method.
6.1 Read the original evidence
Table I. Video quality and clip-generation latency must be read together. Original paper, p. 5 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. Read each task as a three-row block: FID and FVD describe generated-video quality, while Time reports seconds per generated clip. Lower is better for all three rows. Column suffixes count DDIM denoising steps. The caption specifies a 90/10 episode split and batch-one timing averaged over 50 inferences on one NVIDIA RTX A6000. Section VI-A explains that the first clip starts from a held-out episode observation, then later clips use the previous generated endpoint. Compare equal denoising counts first, then inspect how many extra UniPi steps are needed to improve its quality. Keep these video-only tests separate from policy rollouts. e07-protocole08-video-resultse12-denoisinge16-real
What it supports. On Push-T, VILP-16 reports FVD 447.56 at 0.21 seconds, compared with UniPi-16's 744.06 at 0.61 seconds. The advantage is not universal across metrics: UniPi-64 achieves the best Move-the-Stack FID and FVD, but takes 2.5 seconds per clip. The table therefore supports a quality–latency tradeoff, not blanket dominance.
Where the evidence stops. These entries do not measure robot task completion, and the table provides no uncertainty for FID or FVD. Its time covers video inference; the complete-policy timings in Tables IV and VII have a different scope.
Table III. The strongest gains combine task videos with additional off-target action labels. Original paper, p. 6 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. Each cell gives max/mean success percentages with standard deviations across three seeds, evaluated on 50 environments per seed. The first statistic selects the highest success during training; the second selects the highest average of five consecutive rollouts. Neither is simply the final checkpoint. The Epochs Rollout column changes from 300 for Small to 100 for Hybrid. Hybrid adds demonstrations with altered goals or objects for Nut-Assembly and purple rather than gray blocks for Arrange-Blocks. VILP and UniPi planners also receive task videos: 200 and 610 episodes respectively for the two tasks. Diffusion Policy uses only action-labeled data. e10-datae11-scarce-results
What it supports. Arrange-Blocks-Hybrid reaches 84.0 ± 0.0 / 77.6 ± 2.8 with VILP-8, compared with Diffusion Policy's 22.7 ± 3.1 / 17.1 ± 1.2. The effect is task-dependent: Diffusion Policy slightly exceeds VILP on Nut-Assembly-Small. The table supports this particular video-rich, action-scarce learning setup.
Where the evidence stops. The methods do not have equal access to unlabeled task information, and Small/Hybrid also changes evaluation cadence. A zero standard deviation on the selected peak does not imply certainty about population success or eliminate checkpoint-selection bias.
Table VII. Physical execution improves, but fewer than half the VILP trials complete both blocks. Original paper, p. 7 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. Begin with the outcome definitions in the caption: Two Blocks Arranged is full task success, while One Block Arranged means only one block reached its goal and therefore counts as failure. The entries are counts out of 15, not percentages. The task uses a Franka Panda to place L and T blocks into an LT arrangement on a blue line, with delta x/y actions and 220 human demonstrations. Initial block positions and orientations vary. The final row times complete video and action generation in seconds, averaged over 50 inferences. Figure 8 supplies selected rollout snapshots, not additional trial statistics. e06-policye16-real
What it supports. VILP-16 completes 7/15 trials and makes one-block-only progress in 6/15 more, at 0.238 seconds per inference. UniPi-4 and UniPi-16 record no completed or one-block-only outcomes and take 0.358 and 1.422 seconds respectively. This is direct physical-task evidence beyond video-quality metrics, with a clearly limited success rate.
Where the evidence stops. Do not add one-block-only failures to the full-success count. Fifteen trials on one task cannot establish broad deployment reliability. The comparison changes both generated plans and inference delay, so it does not isolate which causes the physical performance gap.
6.2 Results and evaluation conditions
| Task & protocol | Reported result | Comparison & interpretation |
|---|---|---|
| Push-T video planning 90/10 episode split; initial conditioning from held-out episodes, then chaining generated clips. Batch-one timing averages 50 inferences on one NVIDIA RTX A6000. | VILP-16: 16.49 / 447.56; 0.21 s FID / FVD (lower better); video-clip latency in seconds | UniPi-16: 25.43 / 744.06; 0.61 s. UniPi-64: 16.72 / 675.85; 2.5 s. These measure generated videos, not executed task success. VILP does not dominate every task: UniPi-64 has the best Move-the-Stack FID/FVD. e07-protocole08-video-results |
| Arrange-Blocks-Hybrid Simulation; 610 planner videos and 76 labeled demonstrations. Three seeds, each evaluated on 50 environments; rollouts every 100 training epochs. | VILP-8: 84.0 ± 0.0 / 77.6 ± 2.8 Max / selected five-rollout mean success rate (%), mean ± standard deviation across seeds | Diffusion Policy: 22.7 ± 3.1 / 17.1 ± 1.2; UniPi-16: 18.7 ± 2.3 / 16.2 ± 1.7. Both statistics select performance during training; they are not fixed-final-checkpoint estimates. Extra video access distinguishes VILP from Diffusion Policy. e10-datae11-scarce-results |
| Arrange-Blocks-Hybrid denoising ablation Table IV; complete video-plus-action inference, timing averaged over 50 calls. Scores use Table III's max/mean definition. | VILP-4: 76.0 / 64.8, 0.073 s; VILP-8: 84.0 / 80.4, 0.125 s Max / selected mean success (%); latency (s) | UniPi-16: 18.0 / 13.2, 1.43 s. Additional denoising trades latency for success here. These scores differ from Table III and should not be merged with its seed summaries; Table IV supplies no uncertainty. e11-scarce-resultse12-denoising |
| Sim Push-T and Can-PickPlace with fully labeled demonstrations Visual imitation learning; VILP/UniPi use 16 denoising steps. Average of last 10 checkpoints, each over three seeds and 50 initial conditions per seed. | VILP without/with low-dimensional observations: Sim Push-T 82.6/88.0; Can 95.7/92.2 Reported success rate (%) | Diffusion Policy CNN/Transformer: Sim Push-T 84/66; Can 97/98. These baseline entries are adapted from prior work under stated matching seeds/setup. Extra state improves Push-T but reduces Can performance; VILP is not uniformly better than Diffusion Policy. No uncertainty accompanies these entries. e13-full-label |
| Can-PickPlace conditioning ablation All variants diffuse in latent space; Table V checkpoint/seed/environment aggregation. | VILP without low-dimensional observations: 95.7 Reported success rate (%) | Conditional concatenation: 61.7; conditioning only on the generated view, without multiview fusion: 60.4. Supports the conditioning design beyond merely choosing latent diffusion, but does not directly measure cross-view temporal synchronization. e13-full-labele14-modules |
| Real-Arrange-Blocks Franka Panda; delta x/y actions; 220 human demonstrations; randomized initial block positions/orientations; 15 trials per method. | VILP-16: 7/15 full successes, 6/15 one-block-only failures; 0.238 s Full successes / trials; one-block-only failures; complete inference latency (s) | UniPi-4 and UniPi-16: 0/15 full successes and 0/15 one-block-only outcomes; 0.358 and 1.422 s respectively. Demonstrates physical execution on one task. Partial arrangements remain failures; timing averages 50 inferences and is not an independent measurement of the robot servo rate. e16-real |
6.3 Ablations and diagnostic examples
Read component removals and qualitative examples within their stated evaluation conditions.
Figure 7. Predicting farther and executing farther are distinct horizon choices. Original paper, p. 6 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. Read the left panel under its caption's control condition: executed action horizon is fixed at 4 while predicted video horizon varies across 3, 6, 9 and 12. The curve peaks at 6 and then falls. The right panel instead varies the number of executed actions. Follow the green circles labeled Video Planning Horizon 9 and the red dashed squares labeled 12; both rise across their tested action horizons. Section V explains why these lengths differ: a pair of sampled video frames yields an intervening action segment. Compare trends within each panel before connecting prediction length with how frequently the policy replans. e06-policye15-horizon
What it supports. Longer predicted videos do not automatically help when the executed action prefix stays fixed. Longer action execution improves the shown configurations, so choosing video and action horizons jointly matters. This supports a task-specific tradeoff between useful plan length, accumulated prediction error, and the computational cost of generating the plan.
Where the evidence stops. The right-panel green legend says video horizon 9, but the p. 7 prose recommends video/action horizons 6/8. The figure does not directly substantiate that pair. This source discrepancy remains unresolved; the curves also have no error bars.
Table VI. Conditioning changes performance even when latent diffusion is held common. Original paper, p. 7 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. All columns already use latent diffusion. Conditional concatenation joins the condition image and noise at the U-Net input; without fusion conditions generation only on observations from the predicted perspective. The dash for Sim Push-T means that multiview ablation does not apply to its single camera. The last two columns distinguish VILP without and with additional low-dimensional observations; both remain visual policies. Follow the Can row to compare conditioning and view fusion while retaining the latent representation. Scores use Table V's aggregation: the last ten checkpoints, each evaluated across three seeds and 50 environment initial conditions per seed. e05-conditioninge13-full-labele14-modules
What it supports. Can-PickPlace falls from 95.7 with VILP without low-dimensional observations to 61.7 with conditional concatenation and 60.4 without fusion. Thus, latent-space generation alone does not explain the reported policy performance. Extra low-dimensional input is not uniformly helpful either: Can drops to 92.2, whereas Sim Push-T improves to 88.0.
Where the evidence stops. The table measures policy success, not the temporal alignment of generated views. It reports no uncertainty and does not present a complete factorial decomposition of conditioning, fusion and low-dimensional input. Treat it as targeted module evidence.
7. Analysis & limitations
7.1 What the evidence leaves open
Figure 7's green action-horizon curve is labeled video horizon 9, while p. 7 recommends video/action horizons 6/8. The plot does not directly verify that recommendation; the discrepancy remains unresolved. Neither plot shows uncertainty. e15-horizon
Artifact tolerance and multimodal competence are inferred by the authors from task outcomes, not isolated intervention tests. Unified cross-task/view generation and universal video-to-action mapping remain future work. e13-full-labele17-future
7.2 Questions for discussion
- Would matching total visual-data access narrow the gap to direct action diffusion?
- How much physical-task improvement survives when inference delay is matched?
- Which horizon setting resolves the Figure 7 legend/prose discrepancy?
8. Reproducibility audit
8.1 Requirements and known gaps
Match the frozen codec, camera-specific encoders/generators, action mapper, DDIM steps, data access and checkpoint-selection rules. The UniPi comparison matches low-level policy and U-Net base channels/multipliers. Table II measures memory at batch 16: Arrange-Blocks uses 10.0 GB versus 82.5 GB for equal five-frame, 96×160 configurations; this is not total training cost. e03-compressione05-conditioninge06-policye07-protocole09-memorye11-scarce-results
The PDF does not give a complete optimizer/learning-rate schedule, total training budget, software versions, numerical channel configuration, task-by-task frame intervals, or exact low-level loss. The source gives enough structure for a proposed implementation, but not a fully specified reproduction recipe. e18-specification
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 whether off-target labels teach the action bridge
Reader-proposed check, not performed: freeze the same planner trained on the 610 Arrange-Blocks videos and compare action mappers trained on the 16 gray-block demonstrations alone, those 16 plus 60 purple-block demonstrations, and an equal-size resampled-gray control with matched optimizer updates. Fix DDIM steps and both horizons; use common held-out initial conditions and at least the source's three seeds. Report a predeclared final-checkpoint success rate alongside the source-style selected maximum and five-rollout mean. If the purple-block condition beats the update-matched resampling control consistently, it supports useful transfer through the action mapper; disappearance of that advantage would weaken this mechanism-specific interpretation of Hybrid gains. e06-policye10-datae11-scarce-resultse18-specification
Check 2: Separate video-plan quality from control latency
Reader-proposed check, not performed: first test in simulation using the shared low-level mapper and matched video/action horizons. Compare native VILP and UniPi against VILP with an imposed planning delay matched to the measured UniPi latency on the same hardware. Hold evaluation initial states and checkpoint selection fixed, and record full task success, generated-video metrics, and actual observation-to-action delay. Table IV motivates this control because complete inference speed and success change together. If delayed VILP loses most of its advantage, timely feedback is a major contributor; if it retains the gap, differences in predicted plans remain a stronger explanation. Resolve and explicitly log the Figure 7 horizon setting before interpreting the comparison. e06-policye07-protocole12-denoisinge15-horizone16-real
8.3 Reading coverage
Visual audit: All eight original PDF pages were rendered and visually inspected, in addition to individually reading all four supplied text chunks. Page 1 verifies title, authors, affiliations and v1 date; page 2 supports diffusion background; pages 3–4 support compression, conditioning, action mapping and evaluation design; page 5 supports video metrics, memory and dataset illustrations; pages 6–7 support action-data counts, success definitions, timing, module/horizon ablations and physical-task details; page 8 supports the physical-rollout snapshots and closes the paper with acknowledgements/references. All Figures 1–8 and Tables I–VII were inspected on those pages. Each of the six final original crops was also viewed, retaining the graphic, table headings, axes and legends; caption-defined protocols and outcome semantics are carried in the reading guides. Figure 2's conditioning paths and Figure 3's observation feedback were checked against Sections IV–V. Figure 7's horizon-9 legend versus the prose's horizon-6 recommendation is explicitly unresolved. No appendix is present; no separate supplement or continuous video was inspected.
PDF pages inspected for this edition: 1, 2, 3, 4, 5, 6, 7, 8. Appendix coverage: not present.
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
- Title, author affiliations and abstract (p. 1)
- I. Introduction (pp. 1–2)
- II. Related Work (p. 2)
- III. Background: Diffusion Models (p. 2)
- IV. Latent Video Planning, A–D (pp. 2–4)
- V. Imitation Learning with Video Planning (p. 4)
- VI. Experiments, A. Video Planning and B. Imitation Learning with Video Planning (pp. 4–7)
- VII. Discussion and Future Work (p. 7)
- VIII. Acknowledgements and References (p. 8)
Outside the original text pass
- Text extraction does not reconstruct figure images; inspect the retained PDF for figures and equation/table layout.
- Separate supplemental material availability has not been fully verified.
- Identity/version scope: the inspected title and ordered authors match the catalog. The supplied artifact is arXiv:2502.01784v1 [cs.RO], dated 3 February 2025; no other revision or edition was supplied or compared.
- Text extraction does not reconstruct figure images. This acquisition limitation was addressed by visually inspecting all eight supplied PDF pages and every final crop, including equation and table layouts.
- Separate supplemental material availability has not been fully verified. No separate supplement was supplied; supplementary videos were not viewed.
- The linked code repository was not inspected, and no experiments were reproduced. The supplied eight-page PDF has no appendix.
The visual audit above records the subsequent illustrated pass.
8.4 Traceable evidence
e01-identityPDF p. 1, title, byline, affiliation footnotes and arXiv margin stamp
The title is VILP: Imitation Learning with Latent Video Planning. Authors are Zhengtong Xu, Qiang Qiu, Yu She; the stamp identifies arXiv:2502.01784v1, 3 Feb 2025. Xu and She are at Purdue's School of Industrial Engineering; Qiu is at its Elmore Family School of Electrical and Computer Engineering, all in West Lafayette, USA.
Go to primary source ↓e02-problemPDF pp. 1–2, Section I, three challenges and contributions; Figure 1
The paper motivates faster receding-horizon video planning, learning the video/action bridge with non-task-matching action data, and accommodating multimodal actions and multiple cameras.
Go to primary source ↓e03-compressionPDF p. 3, Section IV-B and Figure 2(a)
VQGAN uses perceptual and patch-adversarial objectives; its image encoder maps to a spatial latent and its decoder reconstructs RGB or depth. The trained codec is frozen during subsequent diffusion training.
Go to primary source ↓e04-diffusionPDF p. 2, Section III and Eq. (1); p. 3, Section IV-C, Eq. (2), Figure 2(b–c)
DDIM reverses noise addition. N future frames sampled at interval Δt become an N×H×W×C latent stack. A 3D-convolution U-Net learns noise prediction on that stack, which is decoded into video.
Go to primary source ↓e05-conditioningPDF pp. 3–4, Section IV-D, conditional loss, attention formulation and per-view generator definition
The model approximates p(z_t|o_t). Modified non-pretrained ResNet-18 encoders are separate per camera; triplicated depth forms three-channel inputs. Embeddings concatenate into c_t; U-Net features supply queries and c_t supplies keys/values. Temporal and spatial dimensions are flattened. Each view has its own diffusion model, conditioned on combined observations; temporal alignment is an author claim.
Go to primary source ↓e06-policyPDF p. 4, Section V, action-mapping equation and Figure 3
Two CNN encoders and one MLP map adjacent observation frames to a Δt-long action segment. The same module covers the plan; the diagram starts from actual o_t. Only N_e predicted actions are executed before new observations close the loop. An explicit action-mapper training loss is absent.
Go to primary source ↓e07-protocolPDF p. 4, Section VI opening, VI-A and Figure 4; p. 5, Table I caption
UniPi uses the same low-level policy and matched U-Net base channels/multipliers; both methods use DDIM. Video experiments split episodes 9:1, initialize from unseen observations and then chain generated endpoints. Table I times batch-one video inference over 50 calls on a single NVIDIA RTX A6000. Move-the-Stack and Push-T videos are real-environment data, not policy rollouts.
Go to primary source ↓e08-video-resultsPDF p. 5, Table I, Push-T and Move-the-Stack FID/FVD/Time rows; VI-A discussion
Push-T VILP-16 reports 16.49 FID, 447.56 FVD and 0.21 s; UniPi-16 reports 25.43, 744.06 and 0.61 s; UniPi-64 reports 16.72, 675.85 and 2.5 s. Move-the-Stack UniPi-64 achieves the best FID 39.04 and FVD 429.36 at 2.5 s; VILP-4 has 40.80, 479.59 and 0.058 s.
Go to primary source ↓e09-memoryPDF p. 5, Table II, Arrange-Blocks rows and caption
With training batch size 16, five frames and image size 96×160, VILP occupies 10.0 GB and UniPi 82.5 GB. This table reports memory, not total wall-clock training cost.
Go to primary source ↓e10-dataPDF p. 5, Section VI-B.1 final paragraph and Figure 6; p. 6, continuation below Figure 7 and discussion item 2
Planner datasets contain 610 Arrange-Blocks and 200 Nut-Assembly video episodes. Nut Small/Hybrid uses 10 versus 10+75 different-goal+75 different-object labeled demonstrations. Arrange Small/Hybrid uses 16 gray-block versus 16+60 purple-block labeled demonstrations. Diffusion Policy trains on action-labeled data only; video and action training can be separated.
Go to primary source ↓e11-scarce-resultsPDF p. 6, Table III, all rows and caption
Reported max/mean percentages average three seeds with standard deviations, using 3×50 environments. Max selects the highest training success; mean selects the highest five-consecutive-rollout average. Rollout intervals are 300 epochs for Small and 100 for Hybrid. Arrange Hybrid: VILP-8 84.0±0.0/77.6±2.8; Diffusion Policy 22.7±3.1/17.1±1.2; UniPi-16 18.7±2.3/16.2±1.7. Nut Small: VILP-4 26.7±1.2/23.3±3.4 versus Diffusion Policy 28.0±4.0/24.3±1.0.
Go to primary source ↓e12-denoisingPDF p. 6, Table IV, Arrange-Blocks-Hybrid Score/Time rows and caption
VILP-4 reports 76.0/64.8 at 0.073 s; VILP-8 84.0/80.4 at 0.125 s; VILP-16 84.0/80.0 at 0.231 s; UniPi-16 18.0/13.2 at 1.43 s. Time covers video and action generation over 50 inferences. Max/mean definitions follow Table III; uncertainty and an explanation of differing table scores are not supplied.
Go to primary source ↓e13-full-labelPDF p. 6, VI-B.1 fully labeled-data and artifact discussion; p. 7, Table V and caption
All video data have action labels for Sim Push-T and Can-PickPlace. Table V averages the last 10 checkpoints with three seeds and 50 initial conditions each. VILP without/with additional low-dimensional observations scores 82.6/88.0 on Push-T and 95.7/92.2 on Can; Diffusion Policy CNN/Transformer scores 84/66 and 97/98. Baselines from the cited Diffusion Policy study are adapted under stated matching setup/seeds. The authors interpret performance as evidence of multimodality and artifact tolerance.
Go to primary source ↓e14-modulesPDF p. 7, Table VI, all rows and caption; Table V caption for aggregation
All ablated methods diffuse in latent space. Can success is 61.7 with conditional concatenation, 60.4 without cross-view fusion, 95.7 for VILP without low-dimensional observations, and 92.2 with them. Push-T is single-view, so without-fusion is inapplicable; its conditional-concatenation score is 72.5. Without fusion means conditioning only on the generated perspective.
Go to primary source ↓e15-horizonPDF p. 6, Figure 7, axes, legend and caption; p. 7, VI-B.1 horizon discussion
The left Sim Push-T plot varies video horizon 3/6/9/12 with action horizon fixed at 4 and peaks at 6. The right plot labels green as video horizon 9 and red as 12, increasing with executed action horizon. The prose instead recommends video/action pairs 6/8 or 12/16. The green-curve label and the first recommendation do not match; no error bars are shown.
Go to primary source ↓e16-realPDF p. 7, VI-B.2 and Table VII with caption; p. 8, Figure 8
A Franka Panda with delta x/y actions learns Real-Arrange-Blocks from 220 demonstrations. Initial L/T block positions and orientations vary; the goal is an LT arrangement on a blue line. VILP-16 achieves 7/15 full successes and 6/15 one-block-only failures, with 0.238 s complete inference. UniPi-4/16 each record 0/15 in both outcome rows, at 0.358/1.422 s. Timing averages 50 inferences. Figure 8 shows selected physical-rollout snapshots.
Go to primary source ↓e17-futurePDF p. 7, Section VII, discussion items 1–3
The authors propose unified multi-task/multi-view generation, universal video-to-action mapping and cross-domain, longer-horizon multimodal learning as future directions rather than demonstrated capabilities.
Go to primary source ↓e18-specificationPDF pp. 3–4, Sections IV–V and VI opening; pp. 5–7, experiment configurations and table captions
The supplied methods and experiments specify architecture families, freezing, data counts, denoising settings and selected evaluation protocols, but not a complete optimizer/schedule, total training budget, software versions, numerical U-Net channel settings, all task-specific frame intervals or exact action-mapper loss. There is no implementation appendix in the supplied PDF.
Go to primary source ↓8.5 Primary sources
VILP: Imitation Learning with Latent Video Planning ↗
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