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BUILDER'S SANDBOX

Core Pattern

AI-generated implementation pattern based on this paper's core methodology.

Implementation pattern included in full analysis above.

MVP Investment

$10K - $14K
6-10 weeks
Engineering
$8,000
GPU Compute
$800
LLM API Credits
$500
SaaS Stack
$300
Domain & Legal
$100

6mo ROI

0.5-1.5x

3yr ROI

5-12x

Computer vision products require more validation time. Hardware integrations may slow early revenue, but $100K+ deals at 3yr are common.

Talent Scout

G

Guoheng Sun

University of Maryland, College Park

T

Tingting Du

University of Wisconsin, Madison

K

Kaixi Feng

University of Maryland, College Park

C

Chenxiang Luo

City University of Hong Kong

Find Similar Experts

Vision-Language experts on LinkedIn & GitHub

Founder's Pitch

"Enhance VLA models with robust multi-layer alignment for superior 3D spatial reasoning in robotics."

Vision-Language ModelsScore: 6View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

2/4 signals

5

Quick Build

4/4 signals

10

Series A Potential

2/4 signals

5

🔭 Research Neighborhood

Generating constellation...

~3-8 seconds

Why It Matters

This research addresses the gap in 3D spatial understanding in Vision-Language-Action models, essential for effective and adaptive robotic manipulation.

Product Angle

Productize by creating a toolkit or service that allows robotics companies to enhance their existing VLA systems with better 3D spatial understanding.

Disruption

This method replaces current 2D confined VLA approaches, offering improved spatial awareness and potentially reducing the reliance on expensive hardware like additional sensors for depth mapping.

Product Opportunity

The market for robotics is vast, including sectors like manufacturing, healthcare, and logistics, which require advanced manipulation capabilities; potential customers include robotics manufacturers and automation solution providers.

Use Case Idea

Develop APIs or features within robotic systems to improve navigation and manipulation tasks by enhancing spatial understanding in environments using this model.

Science

The paper introduces ROCKET, which leverages multi-layer alignment using a shared projector to minimize gradient interference. This technique integrates 3D spatial information into VLA models, overcoming the limitations of single-layer alignment.

Method & Eval

ROCKET is tested across datasets like LIBERO and RoboTwin, achieving state-of-the-art success rates at a fraction of the compute cost of existing methods, illustrating its efficiency and efficacy.

Caveats

Success hinges on effectively integrating with heterogeneous robotics hardware and adapting to varied environmental contexts, which might demand further customization.

Author Intelligence

Guoheng Sun

University of Maryland, College Park
ghsun@umd.edu

Tingting Du

University of Wisconsin, Madison

Kaixi Feng

University of Maryland, College Park

Chenxiang Luo

City University of Hong Kong

Xingguo Ding

St. Paul’s School

Zheyu Shen

University of Maryland, College Park

Ziyao Wang

University of Maryland, College Park

Yexiao He

University of Maryland, College Park

Ang Li

University of Maryland, College Park
angliece@umd.edu

References (56)

[1]
Depth Anything 3: Recovering the Visual Space from Any Views
2025Haotong Lin, Sili Chen et al.
[2]
LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models
2025Senyu Fei, Siyin Wang et al.
[3]
InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy
2025Xinyi Chen, Yilun Chen et al.
[4]
Spatial Forcing: Implicit Spatial Representation Alignment for Vision-language-action Model
2025Fuhao Li, Wenxuan Song et al.
[5]
MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation
2025Hao Shi, Bin Xie et al.
[6]
GeoVLA: Empowering 3D Representations in Vision-Language-Action Models
2025Lin Sun, Bin Xie et al.
[7]
ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning
2025Chi-Pin Huang, Yueh-Hua Wu et al.
[8]
$\pi^3$: Permutation-Equivariant Visual Geometry Learning
2025Yifan Wang, Jianjun Zhou et al.
[9]
VOTE: Vision-Language-Action Optimization with Trajectory Ensemble Voting
2025Juyi Lin, Amir Taherin et al.
[10]
WorldVLA: Towards Autoregressive Action World Model
2025Jun Cen, Chaohui Yu et al.
[11]
RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
2025Tianxing Chen, Zanxin Chen et al.
[12]
MLLMs Need 3D-Aware Representation Supervision for Scene Understanding
2025Xiaohu Huang, Jingjing Wu et al.
[13]
UniVLA: Learning to Act Anywhere with Task-centric Latent Actions
2025Qingwen Bu, Yanting Yang et al.
[14]
3D CAVLA: Leveraging Depth and 3D Context to Generalize Vision Language Action Models for Unseen Tasks
2025V. Bhat, Yushi Lan et al.
[15]
Dita: Scaling Diffusion Transformer for Generalist Vision-Language-Action Policy
2025Zhi Hou, Tianyi Zhang et al.
[16]
GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
2025Nvidia, Johan Bjorck et al.
[17]
VGGT: Visual Geometry Grounded Transformer
2025Jianyuan Wang, Minghao Chen et al.
[18]
Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
2025Moo Jin Kim, Chelsea Finn et al.
[19]
Layer by Layer: Uncovering Hidden Representations in Language Models
2025Oscar Skean, Md Rifat Arefin et al.
[20]
SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model
2025Delin Qu, Haoming Song et al.

Showing 20 of 56 references