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K
Koki Seno
Keio University
T
Tomoya Kaichi
KDDI Research Inc.
Y
Yanan Wang
KDDI Research Inc.
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Founder's Pitch
"LILAC translates natural language into robotic actions using optical flow for efficient task execution."
Commercial Viability Breakdown
0-10 scaleHigh Potential
2/4 signals
Quick Build
4/4 signals
Series A Potential
2/4 signals
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Why It Matters
This research enables more natural and adaptive interactions between humans and robots by allowing robots to execute complex tasks based on verbal instructions, reducing the need for highly specific programming or datasets.
Product Angle
LILAC can be productized as a robotics software package for manufacturers of domestic service robots, enabling them to enhance functionality with language-guided movements.
Disruption
LILAC could replace existing less flexible robot programming methods that require exhaustive pre-training datasets and manual coding for each new task.
Product Opportunity
The market is driven by the growing need for adaptable and interactive robots in residential and small business settings. Companies looking to decrease labor costs and increase automation efficiency are potential buyers.
Use Case Idea
Integrate LILAC into consumer robots or drones for home or warehouse automation that responds to spoken instructions.
Science
LILAC is a Vision-Language-Action model that uses a combination of 2D optical flow predictions from images and language inputs to compute robot trajectories. By implementing Semantic Alignment Loss and Prompt-Conditioned Cross-Modal Adapter, it aligns language instructions with visual cues to generate efficient motion paths.
Method & Eval
LILAC was evaluated against benchmarks like Fractal and BridgeData V2, outperforming previous state-of-the-art models in task success rate and optical flow accuracy.
Caveats
The system may struggle with highly ambiguous or unexpected instructions and requires fine-tuning for specific hardware environments. Additionally, visual prompt generation relies on perfect input data quality, which could pose limitations in less controlled environments.
Author Intelligence
Motonari Kambara
LEAD Keio University
motonari.k714@keio.jp
Koki Seno
Keio University
Tomoya Kaichi
KDDI Research Inc.
Yanan Wang
KDDI Research Inc.
Komei Sugiura
Keio University