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arxiv: 2606.25295 · v1 · pith:NNBJGEEMnew · submitted 2026-06-24 · 💻 cs.RO

DynaMOMA: Instantaneous Prediction of Grasp Poses for Mobile Manipulation of Dynamic Objects

Zhinan Yu , Junyan Xu , Jiazhao Zhang , Zheng Qin , Yijie Tang , Yuhang Huang , Yihan Cao , Zhiyuan Yu
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Yongjun Wang Renjiao Yi Chenyang Zhu Kai Xu
This is my paper

Pith reviewed 2026-06-25 21:31 UTC · model grok-4.3

classification 💻 cs.RO
keywords mobile manipulationdynamic objectsgrasp trajectory predictiondiffusion modelreinforcement learningwhole-body controlanticipation reward
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The pith

Coupling an anchor-based diffusion model for grasp prediction with a whole-body reinforcement learning policy enables mobile robots to handle dynamic objects.

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

The paper sets out to show that predicting short-horizon grasp trajectories from past observations alone can supply a whole-body control policy with the information needed to catch moving targets. The prediction step uses a diffusion process anchored to produce consistent sequences, which are then compressed and passed to the policy. An auxiliary reward term shifts the policy's target forward in time to the predicted location rather than the current observation. If the coupling works, the robot can coordinate its base and arm without requiring perfect real-time sensing of the object's future path. This matters because many practical tasks involve objects whose positions change while the robot is approaching.

Core claim

The paper claims that an anchor-based diffusion model conditioned only on historical observations can generate temporally consistent short-horizon grasp trajectories, which when encoded as compact features and supplied to a whole-body reinforcement learning policy equipped with an anticipation-guided reward, produce effective mobile manipulation of dynamic objects, with the approach showing strong results across simulation settings and generalizing to real-world trials.

What carries the argument

The anchor-based diffusion model that generates temporally consistent short-horizon grasp trajectories from historical observations, which are encoded and fed to the reinforcement learning policy.

If this is right

  • The combined predictor and policy achieve strong performance across diverse simulation settings and grasping metrics.
  • Both the predictor and the policy transfer with strong generalizability to physical robot hardware.
  • The anticipation-guided reward gives the policy an explicit short-term horizon that improves coordination between base and arm.
  • The framework handles the core difficulty of evolving target poses without requiring separate modules for navigation and reaching.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same prediction-plus-policy structure could be tested on related dynamic tasks such as pushing or intercepting objects.
  • Replacing the diffusion model with other generative predictors might reveal whether the anchor mechanism is essential or whether any temporally consistent forecaster would suffice.
  • Extending the prediction horizon or adding multi-object handling would be a direct next measurement of the approach's limits.
  • The encoding step that compresses trajectories into features for the policy could be inspected to see how much information is lost versus retained.

Load-bearing premise

That observations from the recent past are enough for the diffusion model to output grasp trajectories that stay useful once the object keeps moving.

What would settle it

Real-world trials in which objects accelerate or change direction faster than the training distribution, causing grasp success rates to fall well below the levels achieved when the predictor is used.

Figures

Figures reproduced from arXiv: 2606.25295 by Chenyang Zhu, Jiazhao Zhang, Junyan Xu, Kai Xu, Renjiao Yi, Yihan Cao, Yijie Tang, Yongjun Wang, Yuhang Huang, Zheng Qin, Zhinan Yu, Zhiyuan Yu.

Figure 1
Figure 1. Figure 1: Illustration of DynaMOMA in real-world mobile manipulation tasks. Top row: Third-person views of the human-to-robot handover task and the tabletop dynamic object grasping task. Bottom row: Chronological first-person point clouds captureed by the wrist-mounted camera at different timestamps (t1, t2, t3). The instantaneous prediction of grasp poses are visualized as sequential grippers, color-coded from gree… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of DynaMOMA. Based on historical contexts, the anchor-based grasp trajec￾tory predictor first generates candidate trajectories {τˆk} K k=1 with confidence scores c. The highest￾scoring trajectory is then selected and encoded with its score into a predictive feature spred. Finally, the whole-body policy integrates Spred, Sprop, Svis, and Sgrasp to output coordinated control actions (Abase, Aarm, Ag… view at source ↗
Figure 3
Figure 3. Figure 3: Experimental setup. Left: parallel simu￾lation environment in Isaac Gym. Right: real-world mobile manipulating system. Static Dynamic Regular Irregular Easy Hard 18 / 22 8 / 10 16 / 20 14 / 22 (81.9%) (80.0%) (80.0%) (63.6%) [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Real-world qualitative results of DynaMOMA. The target object is annotated by a bounding box in the first frame of each row. a clusttered tabeltop. For dynamic scenes, the tasks are divided into easy and hard modes based on human interaction profiles. In the easy mode, the user seamlessly hands over the object to the robot. In the hard mode, the user actively exhibits adversarial actions, such as moving th… view at source ↗
read the original abstract

Mobile manipulation is a fundamental robotics task and has advanced rapidly in recent years, enabling robots to navigate, reach, and interact with objects in complex environments. However, mobile manipulation of dynamic objects remains highly challenging, as robots must coordinate the mobile base and arm while adapting to continuously evolving target poses. A key challenge lies in predicting temporally consistent short-horizon grasp trajectories from dynamic observations. In this work, we propose \ours{}, a dynamic mobile manipulation framework that couples instantaneous grasp trajectory prediction with whole-body control policy. Our predictor uses an anchor-based diffusion model to generate temporally consistent short-horizon grasp trajectories conditioned on historical observations. The predicted trajectories are then encoded as compact features and fed to a whole-body reinforcement learning policy, which controls the mobile manipulator for dynamic grasping. We further introduce a anticipation-guided reward that equips the policy with an anticipatory grasping horizon by adaptively shifting the target from the current grasp observation to the instantaneously predicted grasp trajectory. Through extensive experiments in Isaac Gym simulation, we show that our method achieves strong performance in mobile manipulation of dynamic objects across diverse settings and grasping metrics. Furthermore, our predictor and policy demonstrate strong generalizability in real-world experiments.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper proposes DynaMOMA, a framework for mobile manipulation of dynamic objects that couples an anchor-based diffusion model for instantaneous prediction of temporally consistent short-horizon grasp trajectories (conditioned on historical observations) with a whole-body reinforcement learning policy. Predicted trajectories are encoded as compact features for the policy, which is trained using an anticipation-guided reward that adaptively shifts the target grasp from current observations to the predicted trajectory. The manuscript claims strong performance across diverse settings and grasping metrics in Isaac Gym simulations, plus strong generalizability in real-world experiments.

Significance. If the empirical results hold with rigorous validation, the work could advance dynamic mobile manipulation by demonstrating a practical integration of diffusion-based trajectory prediction and whole-body RL control, particularly through the anticipation-guided reward mechanism. This addresses a key challenge in coordinating base and arm for evolving object poses. The approach builds on existing diffusion and RL techniques in a coherent architecture without introducing circular derivations.

major comments (2)
  1. [Abstract] Abstract: The central empirical claim of 'strong performance' and 'strong generalizability' is asserted without any quantitative metrics, baselines, error bars, ablation studies, or specific grasping metrics, which directly undermines the ability to evaluate whether the data support the claims as stated.
  2. [Abstract] Abstract (paragraph on predictor and policy coupling): The assumption that historical observations alone suffice to produce temporally consistent short-horizon grasp trajectories that remain useful when encoded for the RL policy under the anticipation-guided reward is presented without explicit testing or sensitivity analysis; this is load-bearing for the claimed coupling and requires verification in the experiments section.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract. We agree that the claims require more concrete support to allow proper evaluation and will revise the abstract accordingly. We address the two major comments point by point below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central empirical claim of 'strong performance' and 'strong generalizability' is asserted without any quantitative metrics, baselines, error bars, ablation studies, or specific grasping metrics, which directly undermines the ability to evaluate whether the data support the claims as stated.

    Authors: We agree that the abstract's phrasing is too qualitative. In the revised version we will replace the generic claims with concise quantitative highlights drawn directly from the experiments (e.g., success rates, grasp-quality metrics, and baseline comparisons with standard deviations), while keeping the abstract within length limits. This change will be limited to the abstract and will not alter any experimental results. revision: yes

  2. Referee: [Abstract] Abstract (paragraph on predictor and policy coupling): The assumption that historical observations alone suffice to produce temporally consistent short-horizon grasp trajectories that remain useful when encoded for the RL policy under the anticipation-guided reward is presented without explicit testing or sensitivity analysis; this is load-bearing for the claimed coupling and requires verification in the experiments section.

    Authors: The experiments section already demonstrates end-to-end performance of the coupled system, but we acknowledge that an explicit sensitivity study isolating the role of historical observations would strengthen the manuscript. We will add a short ablation (varying the length of the observation history while keeping all other components fixed) and report the resulting changes in trajectory consistency and policy success rate. This addition will appear in the experiments section and will not require new data collection. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper describes an empirical robotics framework: an anchor-based diffusion predictor for grasp trajectories conditioned on history, encoded into a whole-body RL policy with an anticipation-guided reward. All performance claims are presented as outcomes of Isaac Gym simulation experiments and real-world tests rather than logical deductions or parameter fits that reduce to their own inputs by construction. No equations, self-definitional steps, or load-bearing self-citations appear in the provided description that would make the reported results circular; the method is framed as a proposed architecture validated externally.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; full manuscript would be required to audit them.

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