Towards Generalizable Robotic Manipulation in Dynamic Environments

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Talent Scout

H

Heng Fang

Huazhong University of Science and Technology

D

Dingkang Liang

Huazhong University of Science and Technology

S

Shangru Li

Huazhong University of Science and Technology

S

Shuhan Wang

Huazhong University of Science and Technology

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References (64)

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Efficient Long-Horizon Vision-Language-Action Models via Static-Dynamic Disentanglement
2026Weikang Qiu, Tinglin Huang et al.
[2]
Dream-VL & Dream-VLA: Open Vision-Language and Vision-Language-Action Models with Diffusion Language Model Backbone
2025Jiacheng Ye, Shansan Gong et al.
[3]
HiF-VLA: Hindsight, Insight and Foresight through Motion Representation for Vision-Language-Action Models
2025Minghui Lin, Pengxiang Ding et al.
[4]
Qwen3-VL Technical Report
2025Shuai Bai, Yuxuan Cai et al.
[5]
RoboChallenge: Large-scale Real-robot Evaluation of Embodied Policies
2025Adina Yakefu, Bin Xie et al.
[6]
InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy
2025Xinyi Chen, Yilun Chen et al.
[7]
ContextVLA: Vision-Language-Action Model with Amortized Multi-Frame Context
2025Huiwon Jang, Sihyun Yu et al.
[8]
HAMLET: Switch your Vision-Language-Action Model into a History-Aware Policy
2025Myungkyu Koo, Daewon Choi et al.
[9]
VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action Model
2025Yihao Wang, Pengxiang Ding et al.
[10]
MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation
2025Hao Shi, Bin Xie et al.
[11]
TTF-VLA: Temporal Token Fusion via Pixel-Attention Integration for Vision-Language-Action Models
2025Chenghao Liu, Jiachen Zhang et al.
[12]
ReconVLA: Reconstructive Vision-Language-Action Model as Effective Robot Perceiver
2025Wenxuan Song, Ziyang Zhou et al.
[13]
Spatial Traces: Enhancing VLA Models with Spatial-Temporal Understanding
2025Maxim A. Patratskiy, A. Kovalev et al.
[14]
DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
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[15]
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2025Jun Cen, Chaohui Yu et al.
[16]
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2025Tianxing Chen, Zanxin Chen et al.
[17]
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2025Yifan Zhang, Ruiping Wang et al.
[18]
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[19]
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[20]
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2025Cunxin Fan, Xiaosong Jia et al.

Showing 20 of 64 references

Founder's Pitch

"A dynamic-aware robotic manipulation system equipped with PUMA architecture for enhanced adaptability in fast-paced environments."

RoboticsScore: 8View PDF ↗

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10

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10

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Why It Matters

This research is crucial as it addresses the significant gap in robotic manipulation for dynamic environments, which is essential for deploying robots in real-world scenarios like assembly lines and human collaborative tasks.

Product Angle

Productization can involve integrating this system into existing robotic platforms as a modular software update that improves performance in dynamic settings, targeting industries like manufacturing and logistics.

Disruption

It can replace less adaptive robotic systems currently functioning only in static environments, thus increasing efficiency and flexibility in various high-demand sectors.

Product Opportunity

The market encompasses manufacturing automation, logistics, and area where robots need to handle non-static tasks. Companies focusing on reducing labor costs would pay for this improved capability.

Use Case Idea

Develop robots capable of dynamic tasks, such as assembly line packing or sorting, which require rapid adaptation to moving objects, reducing the need for human intervention.

Science

The paper introduces a dataset called DOMINO and a model named PUMA that enhances vision-language-action models to operate in dynamic environments by incorporating historical optical flow data for better motion prediction and time-sensitive interaction.

Method & Eval

The effectiveness of the proposed model, PUMA, was evaluated using the DOMINO benchmark, showing a 6.3% improvement in success rates over existing models, highlighting its strength in handling dynamic tasks.

Caveats

Challenges include ensuring robustness in highly unpredictable, real-world dynamic settings, and maintaining performance across a wide variety of dynamic conditions not present in the dataset.

Author Intelligence

Heng Fang

Huazhong University of Science and Technology
hengfang@hust.edu.cn

Dingkang Liang

Huazhong University of Science and Technology
dkliang@hust.edu.cn

Shangru Li

Huazhong University of Science and Technology

Shuhan Wang

Huazhong University of Science and Technology

Xuanyang Xi

Huawei Technologies Co. Ltd

Xiang Bai

Huazhong University of Science and Technology

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