# Learning Generalizable Pivoting Skills

arXiv: https://arxiv.org/abs/2305.02554 · HTML: https://mlsanity.com/paper/2305.02554/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2023.

## Built on by (most cited first, 7 of 7)

- [TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction](https://mlsanity.com/paper/2405.10315/) (2024)
- [DyWA: Dynamics-adaptive World Action Model for Generalizable Non-prehensile Manipulation](https://mlsanity.com/paper/2503.16806/) (2025)
- [Learning Extrinsic Dexterity with Parameterized Manipulation Primitives](https://mlsanity.com/paper/2310.17785/) (2023)
- [Diff-Transfer: Model-based Robotic Manipulation Skill Transfer via Differentiable Physics Simulation](https://mlsanity.com/paper/2310.04930/) (2023)
- [Leveraging Extrinsic Dexterity for Occluded Grasping on Grasp Constraining Walls](https://mlsanity.com/paper/2507.14721/) (2025)
- [KEA: Keeping Exploration Alive by Proactively Coordinating Exploration Strategies](https://mlsanity.com/paper/2503.18234/) (2025)
- [Learning Pivoting Manipulation with Force and Vision Feedback Using Optimization-based Demonstrations](https://mlsanity.com/paper/2508.01082/) (2025)

## Abstract (arXiv)

The skill of pivoting an object with a robotic system is challenging for the external forces that act on the system, mainly given by contact interaction. The complexity increases when the same skills are required to generalize across different objects. This paper proposes a framework for learning robust and generalizable pivoting skills, which consists of three steps. First, we learn a pivoting policy on an ``unitary'' object using Reinforcement Learning (RL). Then, we obtain the object's feature space by supervised learning to encode the kinematic properties of arbitrary objects. Finally, to adapt the unitary policy to multiple objects, we learn data-driven projections based on the object features to adjust the state and action space of the new pivoting task. The proposed approach is entirely trained in simulation. It requires only one depth image of the object and can zero-shot transfer to real-world objects. We demonstrate robustness to sim-to-real transfer and generalization to multiple objects.