# A Framework for Robot Manipulation: Skill Formalism, Meta Learning and Adaptive Control

arXiv: https://arxiv.org/abs/1805.08576 · HTML: https://mlsanity.com/paper/1805.08576/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2018.

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

- [TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulation](https://mlsanity.com/paper/2409.11047/) (2024)
- [AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer](https://mlsanity.com/paper/2302.04903/) (2023)
- [Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertainty](https://mlsanity.com/paper/2106.04306/) (2021)
- [A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm](https://mlsanity.com/paper/2310.00708/) (2023)
- [Seq2Seq Imitation Learning for Tactile Feedback-based Manipulation](https://mlsanity.com/paper/2303.02646/) (2023)
- [HACMan++: Spatially-Grounded Motion Primitives for Manipulation](https://mlsanity.com/paper/2407.08585/) (2024)
- [Learning Robotic Manipulation Skills Using an Adaptive Force-Impedance Action Space](https://mlsanity.com/paper/2110.09904/) (2021)
- [Impedance Primitive-augmented Hierarchical Reinforcement Learning for Sequential Tasks](https://mlsanity.com/paper/2508.19607/) (2025)
- [Zero-Shot Transfer of Haptics-Based Object Insertion Policies](https://mlsanity.com/paper/2301.12587/) (2023)
- [Fine Robotic Manipulation without Force/Torque Sensor](https://mlsanity.com/paper/2301.13413/) (2023)
- [A Contact Model based on Denoising Diffusion to Learn Variable Impedance Control for Contact-rich Manipulation](https://mlsanity.com/paper/2403.13221/) (2024)
- [MuTT: A Multimodal Trajectory Transformer for Robot Skills](https://mlsanity.com/paper/2407.15660/) (2024)
- [Learning Human-Inspired Force Strategies for Robotic Assembly](https://mlsanity.com/paper/2303.12440/) (2023)
- [LEMMo-Plan: LLM-Enhanced Learning from Multi-Modal Demonstration for Planning Sequential Contact-Rich Manipulation Tasks](https://mlsanity.com/paper/2409.11863/) (2024)
- [Cognitive Manipulation: Semi-supervised Visual Representation and Classroom-to-real Reinforcement Learning for Assembly in Semi-structured Environments](https://mlsanity.com/paper/2406.00364/) (2024)
- [Tactile Memory with Soft Robot: Robust Object Insertion via Masked Encoding and Soft Wrist](https://mlsanity.com/paper/2601.19275/) (2026)
- [Meta-Learning and Meta-Reinforcement Learning -- Tracing the Path towards DeepMind's Adaptive Agent](https://mlsanity.com/paper/2602.19837/) (2026)

## Abstract (arXiv)

In this paper we introduce a novel framework for expressing and learning force-sensitive robot manipulation skills. It is based on a formalism that extends our previous work on adaptive impedance control with meta parameter learning and compatible skill specifications. This way the system is also able to make use of abstract expert knowledge by incorporating process descriptions and quality evaluation metrics. We evaluate various state-of-the-art schemes for the meta parameter learning and experimentally compare selected ones. Our results clearly indicate that the combination of our adaptive impedance controller with a carefully defined skill formalism significantly reduces the complexity of manipulation tasks even for learning peg-in-hole with submillimeter industrial tolerances. Overall, the considered system is able to learn variations of this skill in under 20 minutes. In fact, experimentally the system was able to perform the learned tasks faster than humans, leading to the first learning-based solution of complex assembly at such real-world performance.