# Learning Task Skills and Goals Simultaneously from Physical Interaction

arXiv: https://arxiv.org/abs/2309.04596 · HTML: https://mlsanity.com/paper/2309.04596/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2023.

## Built on by (most cited first, 1 of 1)

- [Learning Coordinated Bimanual Manipulation Policies using State Diffusion and Inverse Dynamics Models](https://mlsanity.com/paper/2503.23271/) (2025)

## Abstract (arXiv)

In real-world human-robot systems, it is essential for a robot to comprehend human objectives and respond accordingly while performing an extended series of motor actions. Although human objective alignment has recently emerged as a promising paradigm in the realm of physical human-robot interaction, its application is typically confined to generating simple motions due to inherent theoretical limitations. In this work, our goal is to develop a general formulation to learn manipulation functional modules and long-term task goals simultaneously from physical human-robot interaction. We show the feasibility of our framework in enabling robots to align their behaviors with the long-term task objectives inferred from human interactions.