Reimagining service delivery in the agentic era with Google Public Sector
cloud.google.com·19h ago
AI Research
arXiv CS.AI·Jun 12·5 min read
Bounding Boxes as Goals: Language-Conditioned Grasping via Neuro-Symbolic Planning
TL;DR
Robots struggle to understand natural language commands in real-time, limiting their effectiveness in various environments. GRASP (Grounded Reasoning and Symbolic Planning) was developed to translate language prompts into actionable goals using a vision-language model and bounding box detection.
✦ Why It Matters
Engineers can leverage GRASP to develop more adaptable robots that understand natural language without extensive training.
Key Takeaways
- Utilize GRASP for language-conditioned robotic tasks.
- Leverage pretrained VLMs to reduce training time.
- Implement bounding box detection for spatial awareness.
- Achieve high success rates without task-specific training.
How It Works
GRASP translates natural language queries into neuro-symbolic goal states by leveraging a pretrained Vision-Language Model. It uses a bounding box detection pipeline to ground these goals in the physical world, allowing robots to understand and act on abstract spatial concepts without needing extensive retraining.