FMimic: Foundation Models are Fine-grained Action Learners from Human Videos

Guangyan Chen; Haoyang Lu; Meiling Wang; Mengxiao Hu; Mengyin Fu; Te Cui; Tianxing Zhou; Yao Mu; Yi Yang; Yufeng Yue

REVIEW 3 major objections 5 minor 4 references

FMimic: Foundation Models are Fine-grained Action Learners from Human Videos

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read FMimic claims that a robot can acquire generalizable, fine-grained manipulation skills from one to five human videos, without predefined motion primitives, and can execute high-precision tasks via contact-aided refinement.

desk verdict A substantial extension of VLMimic with genuinely new modules, but the headline human-video claim is only tested on thin real-world data; the RLBench numbers use robot videos. read the letter →

arxiv 2507.20622 v1 pith:ONP2L35F submitted 2025-07-28 cs.RO

Guangyan Chen , Meiling Wang , Te Cui , Yao Mu , Haoyang Lu , Zicai Peng , Mengxiao Hu , Tianxing Zhou

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Mengyin Fu Yi Yang Yufeng Yue

This is my paper · ORCID

classification cs.RO

keywords VisualimitationlearningVision-languagemodelsFine-grainedactionKeypointtransferContact-aidedposeoptimizationSkilladaptationHumanvideosRoboticmanipulation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

FMimic is a pipeline for visual imitation learning that tries to move vision-language models (VLMs), models that reason over images and text, from high-level planners to fine-grained action learners. Its claim is that a robot can watch one to five videos of a person doing a manipulation task, extract the underlying movements, and re-target those movements onto new objects and scenes well enough to beat methods trained on a hundred robot demonstrations. The paper reports success rates of 75% with one video and 87% with five videos across RLBench tasks, substantial gains over planner-based and learning-based baselines, and success on long-horizon and high-precision tasks that such baselines largely fail. If the claim is right, robot skill acquisition no longer depends on collecting large paired robot-action datasets or hand-coding motion primitives. Instead, the key technical challenge shifts to grounding human-object interactions and adapting them to the robot's embodiment and the new scene.

What carries the argument

The load-bearing representation is the keypoint-waypoint interaction: keypoints are 6D frames rigidly attached to objects at contact or affordance locations, and waypoints are the compressed relative trajectory of those keypoints, compressed with the SQUISHE algorithm. Everything else, including grounding, VLM prompting, skill transfer, iterative adaptation, and contact refinement, exists to produce and consume this representation. Keypoints are computed from master-slave contact geometry, and waypoints are generated from keypoint trajectories in the master object frame, so the skill is expressed relative to objects rather than in absolute coordinates. The second mechanism that carries the high-precision claim is the contact-based pose optimizer: the paper reframes the absolute pose error of two objects as a single in-hand relative pose error, estimates it with a particle filter over contact observations, and selects which contact to make by maximizing information gain.

What would settle it

Measure the actual grounding error on the demonstration videos, for example by comparing estimated hand and object poses against AprilTag or kinematic ground truth in the real scenes, and check whether the reported success rates survive at the measured error level instead of the injected-noise proxy.

Watch

Extended reading notes

Core claim

The central discovery, stated on its own terms, is that fine-grained manipulation skills can be represented as hierarchical constraints on distilled interaction points, rather than as full trajectories or predefined primitives. From a human video, FMimic grounds human-object contacts by estimating hand poses, object poses, and masks, then distills the motion into keypoints attached to objects and waypoints tracing relative motion. A vision-language model reads visualizations of these keypoints and waypoints, together with their numerical values, to produce semantic constraints (what the motion means) and geometric constraints (math and code that regenerate the motion from current object properties). In a novel scene, keypoints are transferred via region-to-keypoint mapping and the constraints are revised by iteratively comparing rendered adapted interactions with the retrieved demonstration. For high-precision tasks, a skill refiner minimizes collisions in the extracted interaction and optimizes the relative pose through master-slave contact iterations. The paper reports that this is the difference between 51% and 82% success on seen high-precision insertion tasks and between 45% and 73% on unseen ones.

Load-bearing premise

The whole method assumes that the initial stage estimating hand poses, object poses, and hand-object segmentations from the videos is accurate enough to yield reliable keypoints and waypoints, even though the paper's robustness experiments inject artificial noise rather than measuring the real error of that stage.

Editorial extensions

If this is right

A single demonstration video can give a usable skill: FMimic reports 75% average success on 12 RLBench tasks with one video, comparable to diffusion policies trained on 100 robot demonstrations.
Five videos push performance to 87% in RLBench single-task, 83% in multi-task, and 84% in seen real-world tasks, suggesting the representation extracts most of what it needs from very few examples.
Skills generalize to unseen scenes and tasks: success rates of 75% in unseen real-world environments and 67% on unseen tasks, versus roughly 10% and 7% to 35% for the compared baselines.
Long-horizon tasks decompose into subtask skills: FMimic succeeds on six long-horizon tasks at 60% overall, where baselines are near zero.
Contact-aided refinement is what unlocks precision: on high-precision insertion tasks, adding the skill refiner raises seen-environment success from 51% to 82% and unseen-environment success from 45% to 73%.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the reported gains hold, the bottleneck for imitation learning shifts from data volume to the quality of grounding, so research effort should concentrate on hand and object pose estimators and segmentation rather than on policy networks.
The keypoint-waypoint representation suggests a testable unification with relational keypoint methods: because both encode constraints relative to objects, a shared library of interaction primitives could transfer across embodiments by re-estimating keypoints instead of retraining policies.
The contact-based pose optimizer could be separated from the video-learning pipeline and applied to any visuomotor system with force sensing, with the information-gain contact selection reducing the number of exploratory touches needed.
Limits the paper itself acknowledges, such as vision foundation models struggling to distinguish object parts like a pan surface from its handle and object reconstruction needing extra recordings for transparent or thin objects, point to open-vocabulary part segmentation and single-view 3D reconstruction as the next bottlenecks.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Two things up front. First, this is a real extension of the authors' VLMimic work: keypoint-waypoint distillation, region-to-keypoint transfer, and a contact-aided skill refiner are concrete, new mechanisms that change how skills are represented and adapted. The contact-based pose optimization, with a particle filter and information-gain contact selection, is the most original piece. Second, the paper's headline claim—that FMimic learns fine-grained skills from human videos—is not actually tested on its primary benchmark. The RLBench experiments, which produce the 39% and 49% improvement numbers, use robot gripper trajectories, not human videos, because human videos are unavailable in simulation. So the simulation evidence supports a weaker claim: the pipeline works from robot video with known gripper trajectories. The only direct human-video evidence is the real-world set, which relies on 10 trials per condition and human-assessed success.

The paper does several things well. The ablations are thorough and each design choice (hierarchical constraints, VLM grouping vs DBSCAN, iterative comparison, number of iterations) is tested. The failure cases and limitations section is honest; they name VFM limitations, object reconstruction issues, and IK errors. The contact-based pose optimization section is the most technically solid, with a clear state-space model, particle filter, and an information-gain criterion for selecting contacts. The high-precision experiments with the skill refiner show a large jump, and that is a genuinely new contribution.

Where it is soft: beyond the RLBench/human-video mismatch, the robustness analysis injects Gaussian noise into the grounding stages but never reports the actual error of FrankMocap or FoundationPose on the demonstration videos, so we do not know whether the injected noise represents reality. The real-world results have no confidence intervals per task; only overall means with small standard deviations. No code or data is released despite promises on the homepage. Some percentage claims are ambiguous (e.g., "exceeds baselines by over 34%"—relative or absolute? against which baseline?). The pipeline has many threshold parameters (ε, γ, NI, M, Np, No, Nc, Nd) and the VLM prompt engineering is not described enough to reproduce exactly. These are fixable with clearer reporting and an appendix, but they matter.

Is the central argument sound? The system is plausible and the evidence is suggestive, not conclusive. The human-video claim needs at least one simulation experiment where human video is used, or a real-robot study with more trials and automated success detection. Without that, the paper should be accepted conditionally, with the expectation that the authors either add such evidence or soften the claim.

Who is this for? Robotics researchers working on imitation learning from video and code-generating policies. The skill refiner will be of interest to people working on contact-rich manipulation. I would read the paper for the contact-based pose optimization alone. Does it deserve a serious referee? Yes. It is a substantial system paper with honest limitations and a credible pipeline. The flaws are in the evaluation, not in the architecture. Send it to review, but flag the human-video testing gap and the missing code/data.

Referee Report

3 major / 5 minor

Summary. FMimic is a visual imitation learning framework that aims to learn fine-grained robot manipulation skills from a small number of human demonstration videos without relying on predefined motion primitives. The pipeline consists of four main components: (i) human-object interaction grounding built on FrankMocap hand pose estimation, FoundationPose object pose estimation, SAM-Track segmentation, and ORB-SLAM3 camera tracking; (ii) a skill learner that distills grounded interactions into keypoints and waypoints and extracts semantic and geometric constraints using VLMs; (iii) a skill adapter that transfers keypoints to novel objects and updates skills through iterative comparison; and (iv) a skill refiner with collision-minimal interaction optimization and contact-based pose optimization for high-precision tasks. The manuscript reports experiments on RLBench simulation, 14 real-world manipulation tasks, unseen-task generalization, long-horizon tasks, high-precision peg-insertion tasks, and practical tasks such as socket insertion and battery placement. The paper also includes ablations, efficiency measurements, and robustness analyses. The central quantitative claims are improvements exceeding 39% on RLBench, 29% on real-world manipulation, 34% on high-precision tasks, and 47% on long-horizon tasks over the compared baselines.

Significance. If the central claim is fully supported, FMimic would represent a meaningful advance: it would show that foundation models can learn fine-grained, generalizable manipulation skills from very few human videos, thereby removing a key bottleneck that forces current VLM-based methods to rely on hand-coded primitives. The paper's strengths include broad benchmark coverage, detailed ablation studies on each design choice, an honest limitations section with real failure cases, and robustness experiments covering viewpoint changes, partial visibility, injected noise, and environmental perturbations. The paper does not provide machine-checked proofs, reproducible code, or released data, so its contribution is entirely empirical. The largest weakness is that the headline RLBench results are obtained from robot videos with known gripper trajectories rather than from human videos, so the simulation evidence does not directly test the paper's central 'human-video' premise. The real-world results use 10 trials per condition with human evaluation, and no code or data are released for independent verification.

major comments (3)

[Manipulation Task Learning, Experimental setup] The manuscript states: 'Due to the unavailability of human videos in simulations, Demo2code and our method utilize robot videos captured from a single-camera perspective during demonstrations, incorporating robot gripper trajectories.' Because of this, Tables 1 and 2—the quantitative basis for the abstract's claim of over 39% improvement on RLBench—evaluate a version of FMimic that bypasses the entire human-object interaction grounding module, including FrankMocap hand pose estimation, SAM-Track hand/object segmentation, and the conversion from hand pose to gripper pose. The simulation results therefore support a weaker claim: FMimic can learn skills from robot demonstration trajectories with known gripper states. To substantiate the actual human-video claim, the authors should either run the complete pipeline on human videos in a suitable simulation environment, or at least quantify the accuracy of the human-object grounding stage on real human videos and show that the downstream skills remain effective under realistic grounding errors.
[Real-world experimental setup; Tables 3-7] Success rates in the real-world experiments are computed from 10 randomized trials per condition and evaluated by human assessors, but the paper does not report inter-annotator agreement, per-task confidence intervals, or released code and data for independent replication. With n=10, a success rate of 0.8 has a binomial standard error of approximately 0.13, which means that several of the reported gaps between FMimic and the baselines are not statistically distinguishable; for example, in Table 3 the SE result for Ours 1v is 0.73 versus 0.43 for Demo2Code, a difference of 0.30 that is within the range of sampling variability at this trial count. The authors should report exact trial counts, per-task binomial confidence intervals, and ideally pre-registered success criteria. If the 10-trial protocol is retained, the conclusions should be phrased accordingly.
[Robustness against cumulative errors; Table 10] The robustness analysis injects Gaussian noise with σ=5 cm and σ=5° into pose estimates, but it never measures the actual accuracy of FrankMocap, FoundationPose, or SAM-Track on the human demonstration videos used in the real-world experiments. Since the entire pipeline is conditioned on the correctness of these grounding stages, a noise-injection study with arbitrary noise parameters does not close the gap. The authors should provide an evaluation of grounding accuracy—for example, keypoint and waypoint errors against manual annotations, or pose errors against a motion-capture reference—on representative human videos, and then connect that measured error to the perturbation levels studied in Table 10. Without such a measurement, the reader cannot tell whether the 5 cm/5° noise level is representative of the system's actual operating conditions.

minor comments (5)

[Abstract and Tables 1-2] The abstract says the method works 'with a single human video,' but in the RLBench experiments the videos are robot videos with gripper trajectories. Please make the demonstration type explicit in the abstract and in the table captions, e.g., 'Ours (robot video)' for simulation and 'Ours (human video)' for real-world experiments, to avoid ambiguity.
[Experimental setup, Baselines] The sentence 'These methods are trained on robot demonstrations using default hyper-parameters, robot demonstrations consist of paired observation and action sequences' is ungrammatical and should be split into two sentences, with the second clarifying what the robot demonstrations consist of.
[Skill Refiner with Contact-aided Optimization] The sentence 'the robot is incrementally advanced toward the designated position of the master object until contact is confirmed by force detection' is missing an article before 'master object'; additionally, the paper should specify whether force detection is performed by the Franka's built-in torque sensors or by an external force-torque sensor.
[Discussion and Limitations, Hand grasp representation] The subsection on hand grasp representation describes a VLM-based finger-to-gripper mapping and a verification module, but it is not stated whether this module is used in the main experiments or only proposed as a future extension; please clarify its role in the evaluated system.
[Related Work and Experiments] The related work discusses keypoint-based methods such as KALM and ReKep, which are conceptually close to FMimic's keypoint-centric representation, but these methods are not included in the experimental comparison; adding them as baselines would strengthen the claim that FMimic outperforms existing foundation-model-based manipulation approaches.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation; the pipeline is a composition of external components and benchmarks, with self-citations that are not load-bearing.

full rationale

FMimic is an engineering pipeline rather than a derivation. The claimed skill learning is operationalized through human-object grounding (FrankMocap, FoundationPose, SAM-Track), keypoint-waypoint extraction, VLM-generated constraints, keypoint transfer, and contact-based refinement. None of the reported equations define the target result in terms of itself: the pose-optimization filter (Eqs. 10-19) estimates relative pose from contact measurements; the keypoint transfer (Eqs. 5-8) uses registration; the information-gain contact selection (Eqs. 20-21) is a decision rule. No fitted parameter is renamed as a prediction. The paper cites the authors' prior VLMimic/GraphMimic work, including the NeurIPS 2024 predecessor, but these citations motivate the extension rather than supply the experimental numbers; the benchmarks (RLBench, FMB, real-world tasks) are external and success rates are measured outcomes. The main validity concern is not circularity: the RLBench setup explicitly says 'Due to the unavailability of human videos in simulations, Demo2code and our method utilize robot videos captured from a single-camera perspective during demonstrations, incorporating robot gripper trajectories,' so the headline human-video claim is not tested there; this is a scope/claim gap, not an equation-level reduction. The paper's own Limitations section also concedes VFM limitations, reconstruction constraints, and IK/collision failures, which are consistent with an honest non-circular report.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the accuracy and reliability of off-the-shelf perception models (FrankMocap, FoundationPose, SAM-Track) and on the reasoning capability of VLMs. These are strong domain assumptions, and the paper does not quantify their actual error rates in the real-world experiments. Several thresholds and hyperparameters are set by hand without sensitivity analysis for most of them.

free parameters (6)

Contact detection threshold epsilon = 2 cm
Used in Eq. 2 to identify contact start and end in video parsing. Chosen by hand, not fitted to data.
Hand trajectory length filter gamma = 5 cm
Segments with shorter hand motion are discarded. Chosen by hand to filter spurious segments.
Maximum skill adapter iterations NI = 4
Number of iterative comparison rounds, set based on ablation in Table 14(e) to balance performance and efficiency.
Particle filter sample count M = 500
Number of particles for contact-based pose estimation in the skill refiner. Chosen by hand.
Contact strategy samples Np and No = 4 positions, 12 orientations
Used to generate contact candidates for information-gain-based selection. Chosen by hand.
Particle filter noise sigma = 1.0 mm, 0.01 rad
Gaussian noise artificially injected into pose prediction during the skill refiner. Chosen by hand.

assumptions (4)

domain assumption VLMs are capable of reliably generating semantic and geometric constraints from rendered keypoints and waypoints.
Central to the skill learner and adapter (Sec. Skill Learner with Hierarchical Representations, Sec. Skill Adapter with Keypoint Transfer and Iterative Comparison).
domain assumption FoundationPose, FrankMocap, and SAM-Track provide sufficiently accurate pose and segmentation outputs.
All interaction grounding depends on these off-the-shelf components (Sec. Human-object Interaction Grounding).
domain assumption Object 3D models are available or can be reconstructed from videos.
Required for object pose estimation and keypoint transfer (Sec. Interaction estimation).
domain assumption The gripper-object contact model and the in-hand pose stability assumption hold during refinement.
Underlies the contact-based pose optimization and the claim of high-precision execution (Sec. Skill Refiner with Contact-aided Optimization).

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Cite this review

Pith. "Pith review of FMimic: Foundation Models are Fine-grained Action Learners from Human Videos." pith.science (2026). https://pith.science/paper/ONP2L35F

@misc{pith2026250720622,
  author       = {Pith},
  title        = {Pith review of: FMimic: Foundation Models are Fine-grained Action Learners from Human Videos},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ONP2L35F}},
  note         = {Machine review of arXiv:2507.20622}
}

read the original abstract

Visual imitation learning (VIL) provides an efficient and intuitive strategy for robotic systems to acquire novel skills. Recent advancements in foundation models, particularly Vision Language Models (VLMs), have demonstrated remarkable capabilities in visual and linguistic reasoning for VIL tasks. Despite this progress, existing approaches primarily utilize these models for learning high-level plans from human demonstrations, relying on pre-defined motion primitives for executing physical interactions, which remains a major bottleneck for robotic systems. In this work, we present FMimic, a novel paradigm that harnesses foundation models to directly learn generalizable skills at even fine-grained action levels, using only a limited number of human videos. Extensive experiments demonstrate that our FMimic delivers strong performance with a single human video, and significantly outperforms all other methods with five videos. Furthermore, our method exhibits significant improvements of over 39% and 29% in RLBench multi-task experiments and real-world manipulation tasks, respectively, and exceeds baselines by more than 34% in high-precision tasks and 47% in long-horizon tasks.

Figures

Figures reproduced from arXiv: 2507.20622 by the authors.

**Figure 1.** Illustration of our FMimic. (a) Typical VIL methods struggle to generalize to unseen environments, and (b) current methods naively utilize VLMs as planners, encounter difficulties in generating low-level actions. (c) FMimic grounds human videos to obtain action movements, and learns skills with fine-grained actions, while the skill adapter updates skills for generalization. (d) FMimic optimizes the interactions and … view at source ↗

**Figure 2.** Illustration of our FMimic. The human-object interaction grounding module captures interaction motions. These interactions are utilized to derive fine-grained action skills through the skill learner. The skill adapter updates the learned skills to facilitate adaptation to novel scenes. For high-precision tasks with stringent constraints, the skill refiner optimizes interactions and pose estimation results for enhanc… view at source ↗

**Figure 3.** Illustration of Human-object interaction grounding module. (a) It recognizes tasks and related objects from human videos, (b) parses videos into multiple segments and identifies discrete subtasks, then (c) captures interactions within each segment. grasp point thumb index X Y Z [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (23 more)

**Figure 4.** Figure 4: Calculation of the 6D gripper pose from the estimated hand pose. on hand trajectory length criteria, thereby maintaining the integrity of extracted segments. Subtask recognition We instruct VLMs to analyze each segment τi , generating a subtask textual description Tτi …

**Figure 5.** Figure 5: Illustration of our FMimic in skill learning and generalization. (a) The human-object interaction grounding module parses videos into multiple segments and captures interaction movements. Then, (b) the skill learner distills interactions into keypoints and waypoints, t…

**Figure 6.** Figure 6: Illustration of keypoint-waypoint extraction. This keypoint-waypoint paradigm facilitates efficient skill acquisition and enables FMimic to accommodate demonstrations across diverse viewpoints. The object point clouds in the camera frame Ot = x Ot C O are derived by tr…

**Figure 7.** Figure 7: Illustration of keypoint transfer. Regional correspondences between the target and reference objects are established, and point set correspondences within these regions are identified to calculate the mapped 6D keypoints. the demonstrations displaying the highest degre…

**Figure 8.** Figure 8: Illustration of the process of fine-grained action correction. Our FMimic exhibits autonomous failure identification and reasoning for rectification. impediment that would result in task failure. Consequently, FMimic implements modifications to the skill parameters, sp…

**Figure 9.** Figure 9: Illustration of collision-minimal interaction optimization. (a) FMimic extracts the perceived trajectory via human-object interaction grounding and subsequently optimizes it for collision minimization. Interaction points are determined by identifying minimal bounding b…

**Figure 10.** Figure 10: Illustration of contact-based pose optimization. (a) The pose estimation uncertainty associated with both objects is integrated and approximated through the in-hand pose estimation procedure. (b) This process iteratively refines the initial pose estimates via master-s…

**Figure 11.** Figure 11: Illustration of the information gain maximization-based contact strategy selection. We initially sample contact strategies on the master objects, followed by the calculation of IG for each strategy. Ultimately, we select the strategy that yields the highest IG for exe…

**Figure 12.** Figure 12: Example qualitative results for real-world manipulation task in seen environments. Open drawer Stack block Open oven Pour from cup to cup Put toy to drawer Open microwave Put tray in oven Turn on oven Sweep table Insert box Brush pan Sauce spread [PITH_FULL_IMAGE:fig…

**Figure 13.** Figure 13: Example qualitative results for real-world manipulation task in unseen environments. and learns reward functions for reinforcement learning (RL), GraphIRL is trained in simulators with paired robot videos; (4) Code as Policy (CaP) (Liang et al. 2023), an LLM-driven me…

**Figure 14.** Figure 14: Example qualitative results for long horizon task. Our FMimic exhibits robust performance in executing long-horizon tasks [PITH_FULL_IMAGE:figures/full_fig_p017_14.png]

**Figure 15.** Figure 15: Objects used in high-precision tasks across (a) seen environments and (b) unseen environments. tasks are evaluated by human assessors, with the success rate calculated based on 10 randomized object positions and orientations. All other settings remain the same as in t…

**Figure 16.** Figure 16: Configuration of various viewpoints. unseen environment scenarios enable the assessment of the method’s adaptability to previously unencountered objects, providing insights into its robustness and flexibility in novel scenarios. The results, as shown in [PITH_FULL_IM…

**Figure 17.** Figure 17: Example qualitative results for practical high-precision tasks. Semantic: Follow a straight trajectory parallel to the drawer's depth. … Maintain a constant vertical height throughout the opening motion, avoiding any upward or downward movement. Geometric: Open the dr…

**Figure 18.** Figure 18: Visualization of skill learning in our FMimic. Our FMimic can generate corresponding functions for different tasks [PITH_FULL_IMAGE:figures/full_fig_p020_18.png]

**Figure 19.** Figure 19: Illustration of robustness against visual and physical perturbations. (II) Knowledge retrieval errors. A knowledge base is constructed utilizing experimental learning data from the RLBench environment. During the testing phase, relevant knowledge is retrieved from thi…

**Figure 20.** Figure 20: Environmental variations in terms of camera viewpoints, lighting, and scenes. Our FMimic exhibits strong robustness against these variations. (a) Demonstration collection scenes (b) Testing scenes Stack block Put fruit on plate Put tray in oven Sweep table Sauce sprea…

**Figure 21.** Figure 21: Ablation Study on object count and spatial distribution variations [PITH_FULL_IMAGE:figures/full_fig_p022_21.png]

**Figure 22.** Figure 22: Demonstrations of task-relevant object reasoning in cluttered environments. We provide keyframes and their corresponding object detection results to VLMs, VLMs accurately reason about task-relevant objects, even in cluttered environments. def Spiral(): ······ max_radi…

**Figure 23.** Figure 23: Comparative illustration of fine-grained motion skill acquisition between FMimic and planner-based approaches. (a) Methods utilize VLMs as planners rely on the provided motion primitives. (b) Our FMimic autonomously acquires fine-grained actions from human videos. vis…

**Figure 24.** Figure 24: Illustration of enhanced grasping pose mapping method [PITH_FULL_IMAGE:figures/full_fig_p026_24.png]

**Figure 25.** Figure 25: Examples of enhanced grasping pose mapping method. Our approach is applicable to diverse grasping situations. Open microwave Open oven Chemistry experiment Collision IK Error IK Error [PITH_FULL_IMAGE:figures/full_fig_p026_25.png]

**Figure 26.** Figure 26: Examples of failure cases [PITH_FULL_IMAGE:figures/full_fig_p026_26.png]

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Reviewed August 6, 2026 · model on record in the stance chip above.

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