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Talent Scout

Y

Yue Yang

University of North Carolina at Chapel Hill

S

Shuo Cheng

Georgia Institute of Technology

Y

Yu Fang

University of North Carolina at Chapel Hill

H

Homanga Bharadhwaj

Carnegie Mellon University

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Robotics experts on LinkedIn & GitHub

References (37)

[1]
Seeing to Act, Prompting to Specify: A Bayesian Factorization of Vision Language Action Policy
2025Kechun Xu, Zhenjie Zhu et al.
[2]
Long-VLA: Unleashing Long-Horizon Capability of Vision Language Action Model for Robot Manipulation
2025Yiguo Fan, Pengxiang Ding et al.
[3]
GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
2025Nvidia, Johan Bjorck et al.
[4]
Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
2025Moo Jin Kim, Chelsea Finn et al.
[5]
Reflective Planning: Vision-Language Models for Multi-Stage Long-Horizon Robotic Manipulation
2025Yunhai Feng, Jiaming Han et al.
[6]
BOSS: Benchmark for Observation Space Shift in Long-Horizon Task
2025Yue Yang, Linfeng Zhao et al.
[7]
π0: A Vision-Language-Action Flow Model for General Robot Control
2024Kevin Black, Noah Brown et al.
[8]
Local Policies Enable Zero-Shot Long-Horizon Manipulation
2024Murtaza Dalal, Min Liu et al.
[9]
RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation
2024Songming Liu, Lingxuan Wu et al.
[10]
OpenVLA: An Open-Source Vision-Language-Action Model
2024Moo Jin Kim, Karl Pertsch et al.
[11]
Octo: An Open-Source Generalist Robot Policy
2024Octo Model Team, Dibya Ghosh et al.
[12]
Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks
2024Murtaza Dalal, Tarun Chiruvolu et al.
[13]
DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
2024Alexander Khazatsky, Karl Pertsch et al.
[14]
FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects
2023Bowen Wen, Wei Yang et al.
[15]
NOD-TAMP: Generalizable Long-Horizon Planning with Neural Object Descriptors
2023Shuo Cheng, Caelan Reed Garrett et al.
[16]
Human-in-the-Loop Task and Motion Planning for Imitation Learning
2023A. Mandlekar, Caelan Reed Garrett et al.
[17]
Generative Skill Chaining: Long-Horizon Skill Planning with Diffusion Models
2023Utkarsh Aashu Mishra, Shangjie Xue et al.
[18]
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
2023Anthony Brohan, Noah Brown et al.
[19]
VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models
2023Wenlong Huang, Chen Wang et al.
[20]
DoReMi: Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment
2023Yanjiang Guo, Yen-Jen Wang et al.

Showing 20 of 37 references

Founder's Pitch

"LiLo-VLA enables robust, zero-shot, long-horizon robot manipulation via modular object-centric skills."

Robotics and AutomationScore: 6View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

3/4 signals

7.5

Quick Build

4/4 signals

10

Series A Potential

2/4 signals

5

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Why It Matters

This research matters because it addresses key challenges in long-horizon manipulation for robots, which is vital for tasks in dynamic, real-world environments where robots navigate and manipulate multiple objects over extended periods.

Product Angle

Productize this by developing a robotics middleware that can be integrated into existing robotic systems to extend their operational capabilities in real-world environments.

Disruption

This approach could replace existing simplistic robotic automation solutions that are not capable of handling complex sequential tasks without significant reprogramming.

Product Opportunity

The market for advanced robotics in domestic and industrial settings is large, particularly as businesses seek to automate complex sequences of tasks. Companies focusing on home automation, warehousing, and logistics could find this solution appealing.

Use Case Idea

Commercial application could include sophisticated home robots capable of handling long sequences of tasks such as setting a table or clearing various items under dynamic conditions, with minimal pre-programming.

Science

LiLo-VLA uses a modular architecture to separate tasks into reaching and interaction phases. The reaching phase uses motion planning to position the robot, while the interaction phase uses vision-language-action models focused on the target object. This reduces dependency on task-specific training and enhances robustness to environmental changes.

Method & Eval

The method was tested on a 21-task benchmark involving long sequences of actions and was evaluated in both simulated environments and real-world tasks, achieving significant improvements over current state-of-the-art methods.

Caveats

Potential limitations include the reliance on specific sensor setups such as wrist-mounted cameras, which might limit situational adaptability, and a potentially cumbersome integration into existing robotics frameworks.

Author Intelligence

Yue Yang

University of North Carolina at Chapel Hill
yygx@cs.unc.edu

Shuo Cheng

Georgia Institute of Technology

Yu Fang

University of North Carolina at Chapel Hill

Homanga Bharadhwaj

Carnegie Mellon University

Mingyu Ding

University of North Carolina at Chapel Hill

Gedas Bertasius

University of North Carolina at Chapel Hill

Daniel Szafir

University of North Carolina at Chapel Hill