# InterPreT: Interactive Predicate Learning from Language Feedback for Generalizable Task Planning

arXiv: https://arxiv.org/abs/2405.19758 · HTML: https://mlsanity.com/paper/2405.19758/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2024.

## Built on by (most cited first, 30 of 31)

- [VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning](https://mlsanity.com/paper/2410.23156/) (2024)
- [LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models](https://mlsanity.com/paper/2503.18971/) (2025)
- [ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning](https://mlsanity.com/paper/2406.19741/) (2024)
- [M2Diffuser: Diffusion-based Trajectory Optimization for Mobile Manipulation in 3D Scenes](https://mlsanity.com/paper/2410.11402/) (2024)
- [Sotopia-RL: Reward Design for Social Intelligence](https://mlsanity.com/paper/2508.03905/) (2025)
- [Leveraging Environment Interaction for Automated PDDL Translation and Planning with Large Language Models](https://mlsanity.com/paper/2407.12979/) (2024)
- [doScenes: An Autonomous Driving Dataset with Natural Language Instruction for Human Interaction and Vision-Language Navigation](https://mlsanity.com/paper/2412.05893/) (2024)
- [Learning Compositional Behaviors from Demonstration and Language](https://mlsanity.com/paper/2505.21981/) (2025)
- [From Pixels to Predicates: Learning Symbolic World Models via Pretrained Vision-Language Models](https://mlsanity.com/paper/2501.00296/) (2024)
- [Language Models can Infer Action Semantics for Symbolic Planners from Environment Feedback](https://mlsanity.com/paper/2406.02791/) (2024)
- [Tru-POMDP: Task Planning Under Uncertainty via Tree of Hypotheses and Open-Ended POMDPs](https://mlsanity.com/paper/2506.02860/) (2025)
- [SLAP: Shortcut Learning for Abstract Planning](https://mlsanity.com/paper/2511.01107/) (2025)
- [Safe and Interpretable Multimodal Path Planning for Multi-Agent Cooperation](https://mlsanity.com/paper/2602.19304/) (2026)
- [Kintsugi: Learning Policies by Repairing Executable Knowledge Bases](https://mlsanity.com/paper/2605.09487/) (2026)
- [CLIMB: Language-Guided Continual Learning for Task Planning with Iterative Model Building](https://mlsanity.com/paper/2410.13756/) (2024)
- [Towards Fast, Memory-based and Data-Efficient Vision-Language Policy](https://mlsanity.com/paper/2503.10322/) (2025)
- [Unifying Deep Predicate Invention with Pre-trained Foundation Models](https://mlsanity.com/paper/2512.17992/) (2025)
- [Neuro-Symbolic Skill Discovery for Conditional Multi-Level Planning](https://mlsanity.com/paper/2410.10045/) (2024)
- [Learning Hierarchical Domain Models Through Environment-Grounded Interaction](https://mlsanity.com/paper/2505.13497/) (2025)
- [PSALM-V: Automating Symbolic Planning in Interactive Visual Environments with Large Language Models](https://mlsanity.com/paper/2506.20097/) (2025)
- [RoboPilot: Generalizable Dynamic Robotic Manipulation with Dual-thinking Modes](https://mlsanity.com/paper/2510.00154/) (2025)
- [Meanings and Measurements: Multi-Agent Probabilistic Grounding for Vision-Language Navigation](https://mlsanity.com/paper/2603.19166/) (2026)
- [StageGuard: Learning Stage Transitions for Long-Horizon Robot Tasks via Agentic Distillation](https://mlsanity.com/paper/2609.20791/) (2026)
- [Visual Error Patterns in Multi-Modal AI: A Statistical Approach](https://mlsanity.com/paper/2412.00083/) (2024)
- [Disambiguating Reference in Visually Grounded Dialogues through Joint Modeling of Textual and Multimodal Semantic Structures](https://mlsanity.com/paper/2505.11726/) (2025)
- [Grounding Vision and Language to 3D Masks for Long-Horizon Box Rearrangement](https://mlsanity.com/paper/2603.23676/) (2026)
- [Learning Transferable Latent User Preferences for Human-Aligned Decision Making](https://mlsanity.com/paper/2605.12682/) (2026)
- [Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning](https://mlsanity.com/paper/2605.15975/) (2026)
- [Foresight: Iterative Reasoning About Clues that Matter for Navigation](https://mlsanity.com/paper/2606.12550/) (2026)
- [TANDEM: Task and Motion Planning with As-Needed Demonstrations for Efficient Vision-Language-Action Model Fine-tuning](https://mlsanity.com/paper/2609.28314/) (2026)

## Abstract (arXiv)

Learning abstract state representations and knowledge is crucial for long-horizon robot planning. We present InterPreT, an LLM-powered framework for robots to learn symbolic predicates from language feedback of human non-experts during embodied interaction. The learned predicates provide relational abstractions of the environment state, facilitating the learning of symbolic operators that capture action preconditions and effects. By compiling the learned predicates and operators into a PDDL domain on-the-fly, InterPreT allows effective planning toward arbitrary in-domain goals using a PDDL planner. In both simulated and real-world robot manipulation domains, we demonstrate that InterPreT reliably uncovers the key predicates and operators governing the environment dynamics. Although learned from simple training tasks, these predicates and operators exhibit strong generalization to novel tasks with significantly higher complexity. In the most challenging generalization setting, InterPreT attains success rates of 73% in simulation and 40% in the real world, substantially outperforming baseline methods.