# Search-Based Task Planning with Learned Skill Effect Models for Lifelong Robotic Manipulation

arXiv: https://arxiv.org/abs/2109.08771 · HTML: https://mlsanity.com/paper/2109.08771/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2021.

## Built on by (most cited first, 20 of 20)

- [Generative Skill Chaining: Long-Horizon Skill Planning with Diffusion Models](https://mlsanity.com/paper/2401.03360/) (2023)
- [DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools](https://mlsanity.com/paper/2203.17275/) (2022)
- [LEAGUE: Guided Skill Learning and Abstraction for Long-Horizon Manipulation](https://mlsanity.com/paper/2210.12631/) (2022)
- [Human-in-the-Loop Task and Motion Planning for Imitation Learning](https://mlsanity.com/paper/2310.16014/) (2023)
- [A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches](https://mlsanity.com/paper/2404.02817/) (2024)
- [Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulation](https://mlsanity.com/paper/2504.00420/) (2025)
- [LiLo-VLA: Compositional Long-Horizon Manipulation via Linked Object-Centric Policies](https://mlsanity.com/paper/2602.21531/) (2026)
- [NOD-TAMP: Generalizable Long-Horizon Planning with Neural Object Descriptors](https://mlsanity.com/paper/2311.01530/) (2023)
- [Out of Sight, Still in Mind: Reasoning and Planning about Unobserved Objects with Video Tracking Enabled Memory Models](https://mlsanity.com/paper/2309.15278/) (2023)
- [Unleashing More Actions via Action Compositional Training for VLA Models](https://mlsanity.com/paper/2607.00351/) (2026)
- [GePA*SE: Generalized Edge-Based Parallel A* for Slow Evaluations](https://mlsanity.com/paper/2301.10347/) (2023)
- [MOSAIC: Skill-Centric Manipulation Planning with Physics Simulation](https://mlsanity.com/paper/2504.16738/) (2025)
- [Recognising Affordances in Predicted Futures to Plan with Consideration of Non-canonical Affordance Effects](https://mlsanity.com/paper/2206.10920/) (2022)
- [A-ePA*SE: Anytime Edge-Based Parallel A* for Slow Evaluations](https://mlsanity.com/paper/2305.04408/) (2023)
- [Offline Skill Graph (OSG): A Framework for Learning and Planning using Offline Reinforcement Learning Skills](https://mlsanity.com/paper/2306.13630/) (2023)
- [Learning Top-k Subtask Planning Tree based on Discriminative Representation Pre-training for Decision Making](https://mlsanity.com/paper/2312.11027/) (2023)
- [Task and Skill Planning: Hierarchical Robot Planning with Black-Box Skills](https://mlsanity.com/paper/2504.17901/) (2025)
- [Affordance-Based Manipulation Planning with Text Goals and Sim-to-Real Generalisation via Real-to-Sim Image Conversion](https://mlsanity.com/paper/2607.11004/) (2026)
- [Deliberate Practice: Learning Robot Skills under a Budget](https://mlsanity.com/paper/2608.13415/) (2026)
- [Safe Task Planning with Long-Term Graph Memory for Embodied Agents](https://mlsanity.com/paper/2609.08444/) (2026)

## Abstract (arXiv)

Robots deployed in many real-world settings need to be able to acquire new skills and solve new tasks over time. Prior works on planning with skills often make assumptions on the structure of skills and tasks, such as subgoal skills, shared skill implementations, or task-specific plan skeletons, which limit adaptation to new skills and tasks. By contrast, we propose doing task planning by jointly searching in the space of parameterized skills using high-level skill effect models learned in simulation. We use an iterative training procedure to efficiently generate relevant data to train such models. Our approach allows flexible skill parameterizations and task specifications to facilitate lifelong learning in general-purpose domains. Experiments demonstrate the ability of our planner to integrate new skills in a lifelong manner, finding new task strategies with lower costs in both train and test tasks. We additionally show that our method can transfer to the real world without further fine-tuning.