# Learning to Compose Hierarchical Object-Centric Controllers for Robotic Manipulation

arXiv: https://arxiv.org/abs/2011.04627 · HTML: https://mlsanity.com/paper/2011.04627/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

## Built on by (most cited first, 17 of 17)

- [Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks](https://mlsanity.com/paper/2110.03655/) (2021)
- [Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives](https://mlsanity.com/paper/2110.15360/) (2021)
- [K-VIL: Keypoints-based Visual Imitation Learning](https://mlsanity.com/paper/2209.03277/) (2022)
- [On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer](https://mlsanity.com/paper/2312.03673/) (2023)
- [Generalization Through Hand-Eye Coordination: An Action Space for Learning Spatially-Invariant Visuomotor Control](https://mlsanity.com/paper/2103.00375/) (2021)
- [Learning to Open and Traverse Doors with a Legged Manipulator](https://mlsanity.com/paper/2409.04882/) (2024)
- [Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models](https://mlsanity.com/paper/2407.02666/) (2024)
- [STEER: Flexible Robotic Manipulation via Dense Language Grounding](https://mlsanity.com/paper/2411.03409/) (2024)
- [RecoveryChaining: Learning Local Recovery Policies for Robust Manipulation](https://mlsanity.com/paper/2410.13979/) (2024)
- [Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment](https://mlsanity.com/paper/2311.01059/) (2023)
- [Redundancy-aware Action Spaces for Robot Learning](https://mlsanity.com/paper/2406.04144/) (2024)
- [Generalizing Object-Centric Task-Axes Controllers using Keypoints](https://mlsanity.com/paper/2103.10524/) (2021)
- [A Simple Approach to Continual Learning by Transferring Skill Parameters](https://mlsanity.com/paper/2110.10255/) (2021)
- [Learning from Physical Human Feedback: An Object-Centric One-Shot Adaptation Method](https://mlsanity.com/paper/2203.04951/) (2022)
- [Multi-Phase Multi-Objective Dexterous Manipulation with Adaptive Hierarchical Curriculum](https://mlsanity.com/paper/2205.13441/) (2022)
- [APC-RL: Exceeding Data-Driven Behavior Priors with Adaptive Policy Composition](https://mlsanity.com/paper/2601.19452/) (2026)
- [Beyond World-Frame Action Heads: Motion-Centric Action Frames for Vision-Language-Action Models](https://mlsanity.com/paper/2605.11809/) (2026)

## Abstract (arXiv)

Manipulation tasks can often be decomposed into multiple subtasks performed in parallel, e.g., sliding an object to a goal pose while maintaining contact with a table. Individual subtasks can be achieved by task-axis controllers defined relative to the objects being manipulated, and a set of object-centric controllers can be combined in an hierarchy. In prior works, such combinations are defined manually or learned from demonstrations. By contrast, we propose using reinforcement learning to dynamically compose hierarchical object-centric controllers for manipulation tasks. Experiments in both simulation and real world show how the proposed approach leads to improved sample efficiency, zero-shot generalization to novel test environments, and simulation-to-reality transfer without fine-tuning.