# Let's Collaborate: Regret-based Reactive Synthesis for Robotic Manipulation

arXiv: https://arxiv.org/abs/2203.06861 · HTML: https://mlsanity.com/paper/2203.06861/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2022.

## Built on by (most cited first, 3 of 3)

- [Optimal Cost-Preference Trade-off Planning with Multiple Temporal Tasks](https://mlsanity.com/paper/2306.13222/) (2023)
- [Sampling-based Reactive Synthesis for Nondeterministic Hybrid Systems](https://mlsanity.com/paper/2304.06876/) (2023)
- [FONDANT: Strong and Best-Effort Planning via Antichains](https://mlsanity.com/paper/2609.35160/) (2026)

## Abstract (arXiv)

As robots gain capabilities to enter our human-centric world, they require formalism and algorithms that enable smart and efficient interactions. This is challenging, especially for robotic manipulators with complex tasks that may require collaboration with humans. Prior works approach this problem through reactive synthesis and generate strategies for the robot that guarantee task completion by assuming an adversarial human. While this assumption gives a sound solution, it leads to an "unfriendly" robot that is agnostic to the human intentions. We relax this assumption by formulating the problem using the notion of regret. We identify an appropriate definition for regret and develop regret-minimizing synthesis framework that enables the robot to seek cooperation when possible while preserving task completion guarantees. We illustrate the efficacy of our framework via various case studies.