Unifying Deep Predicate Invention with Pre-trained Foundation Models
cs.RO
Submitted: 2025-12-19
Updated: 2026-09-25
Code: https://github.com/thu-ml/RDT2
Project page: https://unipred.github.io
Terminology
Sources
- DINOv3
- GPT-4 Technical Report
- VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning
- From Pixels to Predicates: Learning Symbolic World Models via Pretrained Vision-Language Models
- InterPreT: Interactive Predicate Learning from Language Feedback for Generalizable Task Planning
- Gemini: A Family of Highly Capable Multimodal Models
- OpenVLA: An Open-Source Vision-Language-Action Model
- RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- Learning Transferable Visual Models From Natural Language Supervision
- PaLM-E: An Embodied Multimodal Language Model
- Guiding Long-Horizon Task and Motion Planning with Vision Language Models
- Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
- SAM 2: Segment Anything in Images and Videos
- Relational inductive biases, deep learning, and graph networks
- Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning
- Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
- A Systematic Study of Large Language Models for Task and Motion Planning With PDDLStream
- SLAP: Shortcut Learning for Abstract Planning
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