---
title: Dynamic Object Manipulation Benchmark
url: https://www.emergentmind.com/topics/dynamic-object-manipulation-benchmark-dom
type: topic
---

# Dynamic Object Manipulation Benchmark

The Dynamic Object Manipulation (DOM) benchmark is a standardized evaluation framework for robotic systems tasked with manipulating moving objects under dynamic, real-time conditions. DOM specifies rigorous protocols, diverse datasets, and temporally aligned multi-modal streams designed to expose the challenges intrinsic to perception, anticipation, and continuous closed-loop control in dynamic manipulation scenarios [2601.22153]. The benchmark enables comparative study and systematic advancement of Vision-Language-Action (VLA) policy architectures, with a well-defined set of metrics, sub-dimensions, and use-cases.

## 1. Benchmark Objectives and Task Taxonomy

DOM’s core purpose is to serve as a testbed for robotic policies requiring:

- **Dynamic perception:** Rapidly interpret per-frame object motion (6D pose and velocity) and evolving spatial relations.
- **Temporal anticipation:** Predict future object states to compensate for computational and sensing latency.
- **Closed-loop control:** Generate and execute continuous, low-latency action streams that remain responsive to changes in object dynamics.

DOM defines nine sub-dimensions along three axes—Interaction, Perception, and Generalization:

| Pillar        | Sub-dimension     | Operational Focus                                      |
|:--------------|:-----------------|:-------------------------------------------------------|
| Interaction   | Closed-loop Reactivity (CR) | Track and catch varying-speed objects         |
| Interaction   | Dynamic Adaptation (DA)     | Respond to sudden velocity/direction changes |
| Interaction   | Long-horizon Sequencing (LS)| Coordinate repeated catch-and-place cycles   |
| Perception    | Visual Understanding (VU)   | Recognize similar objects under motion       |
| Perception    | Spatial Reasoning (SR)      | Infer object relations in clutter           |
| Perception    | Motion Perception (MP)      | Estimate speed and trajectory in real time  |
| Generalization| Visual Generalization (VG)  | Transfer to novel shapes and textures        |
| Generalization| Motion Generalization (MG)  | Generalize to new velocity/friction regimes |
| Generalization| Disturbance Robustness (DR) | Operate under external perturbations        |

This taxonomy enables systematic stress-testing across physically and semantically diverse manipulation scenarios [2601.22153].

## 2. Data Collection Pipeline

DOM data is generated via large-scale simulation and teleoperation-free real-world setups, both orchestrated by a uniform four-stage state-machine controller:

- **Synthetic Data (200 K episodes):**
  - **Environments:** 2.8 K tabletop scenes (3D-FRONT), pruned for physical plausibility.
  - **Objects:** 206 everyday items (Objeverse), with randomized textures.
  - **Dynamics:** Object speeds sampled from [0, 0.75] m/s, friction coefficients from [0.5, 1.5], multiple objects per scene.
  - **Sensors:** Three RGB cameras (two external, one wrist-mounted), 25 FPS, 480×360, Azure Kinect intrinsics, randomized illumination parameters.
  - **Ground Truth:** 6D pose and velocity at 25 Hz via Isaac Sim.
  - **Procedure:** For each episode—(1) approach/predict object, (2) grasp and lift, (3) place, (4) reset.

- **Real-World Data (2 K episodes):**
  - **Objects:** 25 household items, both target and distractor.
  - **Sensors:** Two Azure Kinect DK and one wrist-mounted RealSense D435i, all calibrated.
  - **State Estimation:** EfficientTAM for segmentation, geometric triangulation for 3D centroids, temporal fit for velocities (~25 Hz state stream).
  - **Protocol:** Human only initiates object motion; remainder is fully automated, matching simulated controller logic.

This dual-source pipeline provides both diversity and fidelity, with real-world streams mirroring simulation for direct transfer and evaluation [2601.22153].

## 3. Dataset Structure and Annotations

Each DOM episode is annotated with temporally aligned, multi-modal streams:

- **Multi-view RGB:** Synchronized images from multiple perspectives at each timestep $t$. Images (e.g., at $t-2$ and $t$) are concatenated for input.
- **Natural Language Prompt:** Instructional text (e.g., "Place the rolling soda can into the wooden box").
- **Proprioceptive State:** 32-dimensional vector encoding the robot end-effector pose.
- **Action Stream:** For each $t$, a chunk $\mathbf{A}_t = \{\mathbf{a}_t, \dots, \mathbf{a}_{t+n}\}$, with each $\mathbf{a}_i$ a 32-dimensional action (6D pose delta + gripper). Each action $\mathbf{a}_i$ is explicitly labeled with its execution timestep $i$.
- **Object State:** Time-stamped 6D pose and velocity for all objects at 25 Hz.

Action/event alignment accounts for inference latency $m$: an action chunk $\mathbf{A}_t$ produced at $t$ is executed at $t+m$. DOM provides granular ground-truth trajectories $\{\mathbf{s}_t\}$ for precise analysis of anticipation and realignment behaviors [2601.22153].

## 4. Evaluation Protocol and Metrics

DOM specifies quantitative metrics for assessing model performance under kinetic and computational constraints:

- **Success Rate (SR):**
  $$
  \mathrm{SR} = \frac{N_{\text{successful trials}}}{N_{\text{total trials}}} \times 100\%
  $$

- **Reaction Latency (RL):** Time from object “event” (e.g., bounce/change) to first policy-corrective action.
- **Trajectory/Temporal Anticipation Error (TAE):**
  $$
  \mathrm{TAE} = \frac{1}{T}\sum_{t=1}^T \bigl\|\hat p_{t+\Delta} - p_{t+\Delta}\bigr\|
  $$
  where $\hat p_{t+\Delta}$ is the model’s prediction and $p_{t+\Delta}$ ground truth.

- **Path Length:** Total end-effector path (meters).
- **Completion Time:** Elapsed time from object motion onset to episode termination.

**Benchmark splits** include: training (synthetic subset), validation (held-out synthetic scenes/objects), synthetic test (1.8K episodes from unseen scenes), and real-world test (20 episodes per configuration, across both Franka and PiPER arms). Generalization is stress-tested on unseen objects (VG), novel dynamic regimes (MG), and perturbations (DR) [2601.22153].

## 5. Core Challenges and Research Use-Cases

DOM exposes foundational challenges in dynamic manipulation:

- **Fast Object Motion:** Up to 0.75 m/s object speeds with required perception–action cycle under 50 ms.
- **Temporal Misalignment:** Nonzero inference delay ($m$) induces perception–execution synchronization errors, addressed via anticipation (e.g., Latent-aware Action Streaming).
- **Continuous Control:** Policies must support overlapping inference and execution (Continuous Inference), as opposed to conventional chunked, open-loop commands.
- **Long-horizon Coherence:** Robustness over sequential, drifting catch–place cycles.
- **Multimodal Reasoning:** Integration of language, visual flow, and proprioceptive feedback in real time.

The benchmark’s design allows researchers to:

- Evaluate VLA model response speed, perception, and adaptation.
- Investigate trade-offs between model size, inference frequency, and low-latency stability.
- Develop and analyze latency-aware algorithms with evaluation of temporal anticipation and realignment heuristics.
- Statistically assess cross-domain generalization performance [2601.22153].

## 6. Comparative Context and Extensions

The DOM benchmark fundamentally differs from prior dynamic or deformable manipulation datasets by combining:

- Large-scale (200K synthetic + 2K real episodes) annotated 6D dynamic data,
- Automated, protocol-matched synthetic and real-world pipelines,
- Structured, temporally granular action labeling for real-time closed-loop policy analysis.

Contrasted with deformable manipulation benchmarks such as DaXBench—which targets fluids, ropes, and cloth with differentiable simulators [2210.13066]—DOM focuses on dynamic rigid object scenarios, requiring precise perception, anticipation, and low-latency control, rather than model-based gradient optimization or high-DoF state estimation as in reduced-order rope/cloth manipulation [2505.17434]. A plausible implication is that DOM enables rigorous evaluation of perception- and latency-limited policies for dynamic manipulation tasks not readily addressed by differentiable-physics-focused or quasi-static benchmarks.

By delineating modality, protocol, and control stream synchrony, DOM provides a reproducible, extensible foundation for the study of real-time dynamic manipulation under Vision-Language-Action paradigms [2601.22153].

Source: https://www.emergentmind.com/topics/dynamic-object-manipulation-benchmark-dom