EnerVerse-AC: Envisioning Embodied Environments with Action Condition
1. Paper overview
In one sentence: EVAC predicts camera observations from supplied robot actions, enabling learned policy evaluation and synthetic data generation while leaving physical fidelity only partly tested. e02e04e05e06e07e11e13e14e15
| At a glance | What to know |
|---|---|
| Research problem | Source description Robot policies require interactive evaluation, making repeated physical deployment or construction of simulation assets expensive. EVAC aims to predict how observations change after supplied actions, supporting policy testing and data augmentation without rebuilding a task-specific simulator. e02 |
| Core mechanism | |
| A key reported result | Bottle retrieval with synthetic training trajectories: 0.36 with augmentation. Policy success rate (fraction). Retrieve a tightly packed water bottle from a paper box and place it on a table. Compare 20 expert demonstration episodes with the same set plus 30% additional synthetic trajectories. 0.28 with expert data alone. The observed increase is 0.08, or 8 percentage points. Table 1 supplies no uncertainty, trial count or seed replication; its local protocol does not explicitly identify the evaluation environment. e13 |
| Reading caution | Author claim The circle-based gripper representation may not transfer to dexterous hands. Authors report degradation beyond roughly 30 single-view or 10 multi-view chunks, attributing the shorter multi-view horizon partly to wrist-camera background motion. Actor-critic reinforcement-learning integration remains unexplored. e10e16 |
Core contributions
Figure 2. Two action-conditioning routes connect robot trajectories to a video diffusion model. Original paper, p. 4 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. Start at Actions on the left and follow its two outgoing routes. The upward Delta arrow leads to encoded and resampled temporal tokens; these meet the reference-image tokens before entering cross-attention blocks. The rightward Proj. arrow instead produces spatial action maps, which join ray maps and encoded observation/history information at the network input. Follow the UNet blocks toward the video decoder to identify the output as generated imagery. The caption explicitly calls the feature merges concatenation, although the diagram marks them with circled plus signs. Section 3.1 supplies the pose-map and delta-token descriptions; Figure 2 is explicitly a simplified single-view illustration. e03e04e05
What it supports. EVAC conditions appearance generation both on projected pose information and on changes between actions. The routes offer spatial alignment and temporal context to the same video model. Nothing in this diagram makes the network an action-generating policy: policy-produced actions enter as conditions and future images leave as predictions.
Where the evidence stops. Do not implement the circled plus marks as elementwise addition solely from their appearance; the caption specifies concatenation. The action-map encoder is named generically in the diagram and described as CLIP in Section 3.1, leaving some implementation detail unresolved.
2. Motivation
2.1 The problem and the proposed response
Robot policies require interactive evaluation, making repeated physical deployment or construction of simulation assets expensive. EVAC aims to predict how observations change after supplied actions, supporting policy testing and data augmentation without rebuilding a task-specific simulator. e02
2.2 What this reading follows
A robot simulator must respond to what the policy does, including unsuccessful actions. EnerVerse-AC, or EVAC, approaches that problem through conditional video generation. It encodes end-effector poses as image-aligned maps, adds temporal action differences, and represents moving cameras with ray maps. Generated observations then feed a separate policy, or become synthetic training examples. The six visuals below connect those design choices to evaluator scores, a small data-augmentation result and two qualitative ablations. Read the evidence at its stated scale: matching task rankings and preserving an object in selected frames are useful observations, but neither supplies a general physical-accuracy guarantee. e02e04e05e06e07e11e13e14e15
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 Benchmarks & simulators. View the current classification.
3.1 Evidence-based assessment
Insufficient evidence to decide
The supplied classification is entirely unassigned, so there is no substantive catalog placement to confirm or reject. Architecture evidence supports an action-conditioned visual world model paired with a separate GO-1 policy. It neither jointly predicts future observations and actions nor extracts actions by inverse dynamics; a unified One Model judgment would conflate the world model with its external policy. e03e04e07
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 |
|---|---|
|
|
4.2 Equations and their role
5. Method in detail
5.1 Trace two complementary descriptions of an action
An end-effector trajectory specifies position, orientation and gripper openness. EVAC first translates that trajectory into camera-aligned graphics: projected positions locate the hands, axes show orientation and circle shading represents gripper state. This gives the video model a spatial description of the desired motion. The second branch encodes changes between consecutive poses and combines those tokens with the reference-image representation in cross-attention. Reader interpretation: spatial maps help answer where the arm should appear, while the temporal branch helps distinguish how it gets there. Figure 9 tests that distinction through pan manipulation. The authors discuss acceleration-sensitive outcomes, but the supplied method specifies consecutive-pose differences, not a complete acceleration estimator. The training configuration identifies v-prediction; it does not supply a full mathematical training loss. e03e04e09e15
Figure 3. Camera rays supply motion context when the wrist's projected gripper barely moves. Original paper, p. 5 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. Read each row across the two example configurations. In the head-camera row, the projected gripper positions change within the image. In the wrist-camera row, the gripper projection stays near the same region because the camera moves with the arm. Each camera image is paired with a colored visualization of its ray map. Compare the wrist maps across configurations: their changed colors provide a cue that the camera pose changed even when the projected end effector remains nearly fixed. Section 3.2 defines the encoded rays through their origins and directions; the figure visualizes values as RGB without supplying a numerical color scale. e04e05
What it supports. An image-plane pose cue alone can be ambiguous for a camera attached to the moving robot. EVAC supplements that cue with camera rays and exchanges features across views. The figure explains why the extra condition is useful, rather than measuring the amount of improvement it produces.
Where the evidence stops. The colors are an illustration of ray values, not a calibrated motion-error heatmap. The source provides no isolated quantitative ray-map ablation here, so this graphic cannot establish the size of a cross-view consistency gain.
5.2 Recover the motion hidden by a wrist camera
A camera attached to a robot arm introduces a subtle conditioning problem. A large motion in world coordinates may produce little movement of the hand's projection in that camera's image, since the camera and hand move together. Figure 3 illustrates this ambiguity: the wrist projection stays almost fixed while the surrounding scene changes. EVAC therefore supplies ray origins and directions derived from camera poses, in addition to action maps. Spatial attention lets the model exchange information among camera views. Reader interpretation: camera geometry restores information that a local pose projection loses; it does not make the output an explicit three-dimensional physics simulation. The appendix lists five views, including two fisheye head cameras, and six ray-map channels. The paper offers the geometric motivation and qualitative examples without isolating a numerical ray-map benefit. e03e05e09
5.3 Keep simulated feedback separate from synthetic supervision
EVAC has two distinct uses. In evaluator mode, a separate GO-1 policy predicts an action chunk from an observation and instruction. EVAC predicts the next images, which become policy input for another iteration; humans then judge the resulting sequence. Success-rate comparisons therefore test the learned feedback environment together with that policy and scoring procedure. In data-engine mode, the policy is not driving the generation loop. A demonstration supplies a real contact image and fixed endpoint; an earlier pose is perturbed, interpolated actions run backward through EVAC, and generated frames are reversed for forward-time imitation learning. Table 1 evaluates the resulting training-data change. Reader interpretation: evaluator agreement and augmentation gains answer different questions, so neither result should stand in for the other. Failure-data coverage matters especially when generated feedback must honestly preserve unsuccessful actions. e06e07e11e13e14
5.4 Training and inference
During training
Training primarily uses AgiBot World, described as over 210 tasks and one million trajectories, supplemented with mined raw-data failures and failures collected during teleoperation or robot inference. Those corpus totals are not an exact EVAC training-subset count. e08
CLIP and the VAE encoder are frozen; the UNet, resampler and linear layers are fine-tuned. Table 2 specifies v-prediction, 1,000 diffusion steps with a linear noise schedule, 320×512 video, Adam at learning rate 0.00005, up to 100,000 steps and gradient-norm clipping at 0.5. e08e09
The 19 input channels comprise four noisy-image latent channels, four conditioning-image channels, four action-map channels, six ray-map channels and one condition-dropout mask. Reported training uses approximately 32 A100 GPUs for two days single-view or eight days multi-view. e08e09
During inference
Generate 16-frame chunks using four historical frames carried from the previous generated chunk. For evaluation, the separate GO-1 policy consumes an initial observation and instruction, predicts actions, then receives EVAC-generated observations in a repeated feedback loop. Rollout stops below an unspecified action threshold; humans assess success. e07e08
The evaluator uses single-view GO-1 without its latent planner. This is a learned observation loop: EVAC predicts consequences, while GO-1 supplies actions. Video-MLLM scoring is suggested, rather than established as the reported evaluation procedure. e07
5.5 Implementation flow
- Encode observations and pose maps
A UNet-based latent video diffusion model predicts future frames. Project end-effector positions through calibrated cameras; orientation axes and a shaded circle encode rotation and gripper openness. Lighter circles mean open, darker closed, with different colors distinguishing arms. Section 3.1 describes CLIP encoding and channel-wise fusion of action-map features with RGB features. e03e04
- Inject temporal action changes
Consecutive-frame pose differences pass through a linear projector and attention-based resampling. Their fixed-length tokens combine with reference-image guidance and condition UNet cross-attention. The paper attributes improved dynamics to these temporal cues, but does not specify a separate acceleration-estimation equation. e04e15
- Represent moving cameras
A wrist-mounted camera can see a nearly stationary end-effector projection even when the arm moves. Camera-ray origins and directions supply motion context; ray maps concatenate with trajectory features, while spatial cross-attention exchanges information across views. The generated representation is multi-view imagery, without an explicit reconstructed physical state. e03e05
- Generate training approaches backward
Segment demonstrations into fetching, grasping and homing using gripper-openness changes. Keep the contact endpoint fixed, perturb an earlier fetching pose, and linearly interpolate actions. Condition on the real contact image, generate along the reversed action sequence, then reverse the frames back into forward-time policy training pairs. e06
6. Experiments & results
EVAC turns robot action sequences into future camera observations using a video diffusion model. Spatial pose maps, temporal action differences and camera rays condition the generated environment. A separate policy can interact with that environment for evaluation, or learn from synthetic trajectories. The strongest evidence is a small policy-data augmentation experiment and agreement with real-robot evaluation trends; physical accuracy remains incompletely measured.
6.1 Read the original evidence
Figure 7. EVAC reproduces the ordering of four task scores and three checkpoint scores. Original paper, p. 7 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. Read the left panel by pairing the gray Real Robot Eval bar with the orange Ours Eval bar for each task. The right panel changes the real-evaluation color to blue and compares policy checkpoints at 4K, 8K and 13K steps on Take a Bottle. Both vertical axes show success percentages. These are two comparisons with different purposes: task ordering on the left and checkpoint progression on the right. The protocol uses a separate single-view GO-1 policy, real-test initial images for EVAC and three human evaluators; Appendix A.4.1 states 40 trials per task with slightly randomized starting conditions. e07e11e12
What it supports. The left-panel real/EVAC rates are 28/25% for bottle, 100/90% for toast, 85/88% for bacon and 55/50% for leaf. Task order agrees. Checkpoint rates also rise in both settings: real 40/61/79% versus EVAC 40/63/76%. This supports trend agreement in the tested setup, with imperfect absolute calibration.
Where the evidence stops. The panels provide no error bars, paired outcome counts or measured correlation coefficient. The checkpoint-specific denominator is not stated. Agreement among these aggregate scores does not show that individual generated outcomes reliably match physical rollouts.
Table 1. A small synthetic-data addition accompanies an eight-point policy success increase. Original paper, p. 8 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. The left column identifies the training-data condition, while the right column reports success as a fraction. Baseline means training with 20 expert demonstration episodes. The second row retains those episodes and adds 30% synthetic trajectories from EVAC. The task described in Section 4.4 is pulling a tightly packed bottle from a paper box and placing it on a table. Read the table together with the augmentation procedure: contact anchors remain fixed, earlier poses are perturbed, and generated backward sequences are reversed into forward training data. The percentage in the row label concerns added trajectories, not the change in success rate. e06e13
What it supports. The reported rate increases from 0.28 to 0.36, an absolute gain of 0.08, or 8 percentage points. This is evidence that the proposed data pipeline can help the policy in this bottle task. It is a downstream policy result rather than a direct image-quality score for EVAC.
Where the evidence stops. No uncertainty, seed count, evaluation trial count or matched-size real-data control accompanies the table. Section 4.4 does not explicitly identify the evaluation environment, so this edition does not relabel its numbers as verified physical-deployment measurements.
6.2 Results and evaluation conditions
| Task & protocol | Reported result | Comparison & interpretation |
|---|---|---|
| Policy evaluator agreement across four retrieval tasks Single-view GO-1 without latent planner; slightly randomized starts, real-test initial frames reused for EVAC, three human evaluators; Appendix A.4.1 states 40 evaluations per task. | EVAC: Bottle 25, Toast 90, Bacon 88, Leaf 50. Reported success rate (%) | Real robot: 28, 100, 85, 55, respectively. Both settings give the same task ordering, although toast differs by 10 percentage points. These aggregate rates do not establish per-rollout agreement, general calibration or a measured correlation coefficient. e07e11 |
| Policy evaluator agreement across training checkpoints Take a Bottle; the same policy evaluated at 4K, 8K and 13K training steps. | EVAC: 40, 63, 76. Reported success rate (%) | Real robot: 40, 61, 79. Both curves improve across these checkpoints. The figure supplies no error bars or checkpoint-specific trial counts; it does not demonstrate detection of arbitrary training instabilities. e12 |
| Bottle retrieval with synthetic training trajectories Retrieve a tightly packed water bottle from a paper box and place it on a table. Compare 20 expert demonstration episodes with the same set plus 30% additional synthetic trajectories. | 0.36 with augmentation. Policy success rate (fraction) | 0.28 with expert data alone. The observed increase is 0.08, or 8 percentage points. Table 1 supplies no uncertainty, trial count or seed replication; its local protocol does not explicitly identify the evaluation environment. e13 |
| Failure-data and delta-action ablations Figure 8 compares training with/without failure trajectories on an empty grasp; Figure 9 compares with/without delta attention during pan manipulation. | Authors report less invented grasp success with failure data and fewer object inconsistencies with delta attention. Qualitative frame consistency | The success-only model hallucinates a grasped bottle; the no-delta sequence has highlighted inconsistencies. These selected sequences support mechanism hypotheses, without measuring population-level error reductions or independently proving acceleration understanding. e14e15 |
6.3 Ablations and diagnostic examples
Read component removals and qualitative examples within their stated evaluation conditions.
Figure 8. Failure examples help prevent an imagined grasp from becoming an imagined success. Original paper, p. 8 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. Compare corresponding columns in the upper W/o Failure and lower With Failure rows, then follow each sequence from left to right. The experiment asks the robot to execute a grasping motion without actually grasping the target bottle. The red dashed circles in the upper row draw attention to generated bottle appearances near the gripper. The lower row keeps the gripper empty as it moves away from the box. Section 4.5 attributes this contrast to training coverage: successful demonstrations alone encourage the model to complete a familiar successful interaction, while collected failure trajectories expose it to actions that do not achieve contact. e08e14
What it supports. A visually plausible future can still be wrong about whether an action succeeded. This example illustrates the evaluator's central risk: the learned environment may invent a successful contact. The authors' failure-data intervention addresses that risk in the displayed case, supporting broader outcome coverage as a training requirement.
Where the evidence stops. This is a selected qualitative comparison, with no reported hallucination frequency or exact failure-data ratio. The source does not establish that data quantity and diversity were matched, so the image alone cannot isolate failure coverage from the benefit of additional training examples.
Figure 9. Delta attention is tested on pan motions that demand temporal consistency. Original paper, p. 13 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.
How to read it. The visible W label belongs to the upper sequence and W/O to the lower sequence, matching the original caption. Each sequence includes generated camera frames followed by the corresponding action-map strip. Compare the food item in the pan at matching columns; dashed red boxes in the lower sequence mark inconsistent or hallucinated regions, while solid boxes emphasize upper-sequence object appearances. Appendix A.2 discusses rapid and slow shaking, tossing and upward shaking as motivating cases. Section 3.1 defines the actual module through differences between consecutive poses and attention tokens, so the architecture supplies temporal cues rather than an explicitly specified physical acceleration solver. e04e15
What it supports. The displayed comparison supports the authors' claim that delta-action conditioning helps preserve objects during dynamic pan manipulation. It connects the temporal branch in Figure 2 to an observable failure mode. The evidence is qualitative: it does not report an error reduction, velocity-estimation accuracy or measured contact-dynamics fidelity.
Where the evidence stops. Selected frames cannot demonstrate continuous-time motion or acceleration accuracy. Although Appendix A.2 invokes second-order dynamics, it supplies no separate second-derivative computation; the implementation description remains consecutive-action differences processed through attention.
7. Analysis & limitations
7.1 What the evidence leaves open
The circle-based gripper representation may not transfer to dexterous hands. Authors report degradation beyond roughly 30 single-view or 10 multi-view chunks, attributing the shorter multi-view horizon partly to wrist-camera background motion. Actor-critic reinforcement-learning integration remains unexplored. e10e16
Four retrieval tasks and one policy family provide narrow evaluator evidence. Attractive images and matching success-rate trends do not establish contact-force accuracy or broad replacement of physical simulation; the paper reports no direct dynamics-error benchmark for these claims. e07e11e12e14e15
LIBERO transfer uses 417 fine-tuning trajectories and qualitative comparisons, without a quantitative benchmark result. Figure 11's failure label says collision while its caption calls it an empty catch; that failure subtype remains unresolved. e17
7.2 Questions for discussion
- Would evaluator ranking survive policies that visit failure states outside the collected action distribution? (e07, e08, e11)
- Does delta attention improve measured contact outcomes when spatial paths are held fixed and only timing changes? (e04, e15)
8. Reproducibility audit
8.1 Requirements and known gaps
Reproduction requires synchronized observations, end-effector trajectories and camera calibration, including moving-camera poses and the raw failure data. Exact failure counts, train/test partitions and overlap controls are not supplied. Use the five views listed in Table 2: head, left/right fisheye head cameras and left/right hand cameras. e04e05e08e09
Resolve the batch-size discrepancy before training: Section 4.1 says 16, whereas Table 2 gives per-GPU batches of 8 single-view and 1 multi-view, without explaining reconciliation. The dropout probability, full loss, inference sampler/step count, action-stop threshold and numerical augmentation range are also unspecified. e03e07e08e09e06
A minimal reader-proposed test would hold scene, trajectories and compute fixed while removing delta attention, then measure object persistence against real recordings. A separate matched-size augmentation control should compare EVAC data with repeated expert examples on held-out real trials. e13e15
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: Proposed check: distinguish timing effects from spatial conditioning
Train matched EVAC variants with and without delta attention using identical data, update counts and camera conditions. Record pan motions that follow the same spatial path at slow and fast timings, including toss-like and shake-like executions. Condition both variants on the corresponding action sequences and shared initial images, matching generation seeds where possible. Compare generated object persistence and tracked object trajectories with the recorded videos using a fixed evaluation procedure. Report both raw errors and uncertainty across trials. If temporal conditioning provides the claimed benefit, the full model should reduce timing-dependent errors beyond the spatial-only variant; equal performance would weaken that mechanism claim. This is a reader-proposed experiment, not a reproduction performed here. e04e15
Check 2: Proposed check: isolate the synthetic-data contribution
Repeat the bottle task with three policy-training conditions: the original 20 expert episodes, repeated expert samples matched to the augmented training budget, and those episodes plus 30% EVAC-generated trajectories. Match policy initialization, update counts and example sampling; preserve the contact anchors and record the actual spatial perturbation range. Evaluate every condition on the same held-out distribution of real initial scenes, with blinded success scoring and several training seeds. Publish trial counts and confidence intervals. A consistent gain over both controls would support useful synthetic variation; a gain only over the smaller-budget baseline would suggest that extra training exposure explains part of Table 1. This is a proposed physical evaluation, not a claim about Table 1's unspecified environment. e06e13
8.3 Reading coverage
Visual audit: Visually inspected the title/author/affiliation page, all Figures 1–18 and Tables 1–2, plus every supporting method, training, evaluation and limitation page. All six final original crops were separately viewed with legends, row labels and table headers retained. Figure 2's concatenation wording was checked against its circled-plus merge marks; Figure 9's visible W/W/O ordering agrees with its caption. Figure 11 retains an unresolved collision-versus-empty-catch label discrepancy. Pages 10–11 contain bibliography text and were read in the complete four-chunk text pass. External videos and separate supplements were not inspected.
PDF pages inspected for this edition: 1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 13, 14, 15, 16, 17, 18, 19, 20. 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 Related Work
- 3 Method; 3.1 Multi-Level Action Condition Injection; 3.2 Multi-View Condition Injection; 3.3 Applications
- 4 Experiments; 4.1–4.5
- 5 Conclusion; 6 Limitations and Discussions
- References
- A.1 Training Details; A.2 More Ablations
- A.3.1–A.3.3 Additional Results
- A.4.1 Policy Evaluator; A.4.2 Data Engine
Outside the original text pass
- Text extraction does not reconstruct figure images; inspect the retained PDF for figures and equation/table layout.
- Identity/version scope: the title and all eleven authors match the catalog. The inspected title page identifies arXiv:2505.09723v1 [cs.RO], 14 May 2025. No alternate revision was supplied, so edition differences were not compared.
- Acquisition limitation: text extraction does not reconstruct figure images. This was addressed by separately inspecting the PDF's figures, equations and tables; bibliography-only pages 10–11 were read as text.
- Separate supplemental material availability has not been fully verified.
- Only the supplied PDF was reviewed. Linked project pages, code, checkpoints, datasets and videos were not accessed; experiments were not reproduced.
The visual audit above records the subsequent illustrated pass.
8.4 Traceable evidence
e01PDF p. 1, title, author block, affiliation footnote and arXiv margin
The title and eleven authors match the supplied catalog; the artifact is arXiv:2505.09723v1 [cs.RO], 14 May 2025. Affiliations read AgiBot, SJTU and MMLab-CUHK.
Go to primary source ↓e02PDF pp. 1–2, Abstract and Section 1
Motivates action-responsive learned visual environments for reducing the cost of interactive robot evaluation and augmenting policy data.
Go to primary source ↓e03PDF p. 3, Section 3, opening formulation
Defines history H, future K, view count V, seven/fourteen-dimensional action trajectories, latent encoding and the schematic conditional denoising relation; the output is future multi-view frames.
Go to primary source ↓e04PDF p. 4, Figure 2 with caption and Section 3.1
Describes calibrated pose projection, colored orientation axes, shaded gripper circles, encoded action-map fusion and delta-action tokens conditioned through UNet cross-attention. The caption specifies concatenation at merges drawn with circled plus symbols.
Go to primary source ↓e05PDF pp. 4–5, Section 3.2 and Figure 3
Spatial attention exchanges cross-view features; wrist projections alone fail to convey arm motion. Rays r=(o_r,d_r) encode origins/directions and are concatenated with trajectory features.
Go to primary source ↓e06PDF p. 5, Section 3.3 Data Engine; p. 20, Section A.4.2 and Figure 18
Gripper openness identifies contact phases. Perturb the pre-contact pose within an unspecified fixed range, interpolate to the fixed contact pose, generate backward from the contact image, then reverse for GO-1 training.
Go to primary source ↓e07PDF pp. 5–6, Section 3.3 Evaluator and Section 4.3; p. 17, Section A.4.1
Separate GO-1 and EVAC alternate actions and observations, stopping below an unspecified action threshold. Real-test initial frames condition EVAC; three evaluators judge success. Single-view GO-1 omits its latent planner; each task has 40 evaluations with slightly randomized starts and successful retrieval as the criterion.
Go to primary source ↓e08PDF p. 6, Section 4.1 Dataset and Implementation Details
AgiBot World is described as over 210 tasks and one million trajectories; additional failures are mined/collected. CLIP and VAE encoder are frozen; other named modules are tuned. Reports batch 16, 32 A100 GPUs for two/eight days, memory four and chunk length sixteen.
Go to primary source ↓e09PDF p. 12, Section A.1 and Table 2, Diffusion/UNet/Data/Training rows
Lists 19 input channels, condition-dropout mask, 320×512 video, five named views, linear 1,000-step diffusion, v-prediction, Adam learning rate 5×10^-5, 100,000 maximum steps, clipping 0.5 and per-GPU batches eight/one.
Go to primary source ↓e10PDF p. 6, Section 4.2; p. 7, Figures 5–6; p. 9, Section 6
Authors describe visual stability for up to 30 single-view or 10 multi-view chunks, followed by artifacts; Figure 6 shows selected chunks 1, 3, 5, 7 and 9.
Go to primary source ↓e11PDF p. 7, Figure 7 left panel and Section 4.3
Real/EVAC rates are Bottle 28/25%, Toast 100/90%, Bacon 85/88% and Leaf 55/50%; no error bars or correlation coefficient are supplied.
Go to primary source ↓e12PDF p. 7, Figure 7 right panel and checkpoint-evaluation paragraph
For Take a Bottle, real/EVAC success at 4K, 8K and 13K steps is 40/40%, 61/63% and 79/76%; checkpoint-specific counts and uncertainty are not given.
Go to primary source ↓e13PDF pp. 7–8, Section 4.4; p. 8, Table 1, both training-data rows
Bottle extraction uses 20 expert episodes versus that set plus 30% synthetic trajectories; success is 0.28 versus 0.36. The local description omits evaluation-environment identification, evaluation counts and uncertainty.
Go to primary source ↓e14PDF p. 8, Section 4.5 and Figure 8 with caption
Empty-grasp comparison shows a hallucinated bottle in the upper W/o Failure row and an empty gripper in the lower With Failure row. The experiment is qualitative.
Go to primary source ↓e15PDF p. 13, Section A.2 and Figure 9 with caption; p. 4, Delta Action Attention Module
Authors compare pan-motion generation with/without delta attention and attribute consistency to temporal/acceleration cues. The image and caption agree: upper W has the module, lower W/O lacks it; dashed red boxes mark lower-row inconsistencies. No numerical ablation metric or explicit second-derivative formula is provided.
Go to primary source ↓e16PDF p. 9, Section 6 Limitations and Discussions
Discusses dexterous-hand adaptation, wrist-camera background complexity, shorter multi-view generation and unexplored actor-critic applications.
Go to primary source ↓e17PDF p. 15, Section A.3.2 and Figure 11 image/caption
LIBERO adaptation uses 417 trajectories and qualitative GT/GEN comparisons. The figure labels its failure Collision while the caption describes an empty catch.
Go to primary source ↓8.5 Primary sources
EnerVerse-AC: Envisioning Embodied Environments with Action Condition ↗
PDF · 6,656 extracted words
Source fingerprint
ae2c7fd06d0beb6ffd66d0f38107dcaa1db1d581db3a2f15f58ec07c83252002