# DeComplex: Task planning from complex natural instructions by a collocating robot

arXiv: https://arxiv.org/abs/2008.10084 · HTML: https://mlsanity.com/paper/2008.10084/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2020.

## Built on by (most cited first, 11 of 11)

- [GPT-4V(ision) for Robotics: Multimodal Task Planning from Human Demonstration](https://mlsanity.com/paper/2311.12015/) (2023)
- [Incremental Learning of Humanoid Robot Behavior from Natural Interaction and Large Language Models](https://mlsanity.com/paper/2309.04316/) (2023)
- [Talk-to-Resolve: Combining scene understanding and spatial dialogue to resolve granular task ambiguity for a collocated robot](https://mlsanity.com/paper/2111.11099/) (2021)
- [VeriGraph: Scene Graphs for Execution Verifiable Robot Planning](https://mlsanity.com/paper/2411.10446/) (2024)
- [What Breaks Embodied AI Security:LLM Vulnerabilities, CPS Flaws,or Something Else?](https://mlsanity.com/paper/2602.17345/) (2026)
- [DoRO: Disambiguation of referred object for embodied agents](https://mlsanity.com/paper/2207.14205/) (2022)
- [Can Visual Context Improve Automatic Speech Recognition for an Embodied Agent?](https://mlsanity.com/paper/2210.13189/) (2022)
- [tagE: Enabling an Embodied Agent to Understand Human Instructions](https://mlsanity.com/paper/2310.15605/) (2023)
- [A Prompt-driven Task Planning Method for Multi-drones based on Large Language Model](https://mlsanity.com/paper/2406.00006/) (2024)
- [A Perspective on Robotic Telepresence and Teleoperation using Cognition: Are we there yet?](https://mlsanity.com/paper/2203.02959/) (2022)
- [MIND: Instruction-Conditioned Incremental Grounding for Robots](https://mlsanity.com/paper/2610.19262/) (2026)

## Abstract (arXiv)

As the number of robots in our daily surroundings like home, office, restaurants, factory floors, etc. are increasing rapidly, the development of natural human-robot interaction mechanism becomes more vital as it dictates the usability and acceptability of the robots. One of the valued features of such a cohabitant robot is that it performs tasks that are instructed in natural language. However, it is not trivial to execute the human intended tasks as natural language expressions can have large linguistic variations. Existing works assume either single task instruction is given to the robot at a time or there are multiple independent tasks in an instruction. However, complex task instructions composed of multiple inter-dependent tasks are not handled efficiently in the literature. There can be ordering dependency among the tasks, i.e., the tasks have to be executed in a certain order or there can be execution dependency, i.e., input parameter or execution of a task depends on the outcome of another task. Understanding such dependencies in a complex instruction is not trivial if an unconstrained natural language is allowed. In this work, we propose a method to find the intended order of execution of multiple inter-dependent tasks given in natural language instruction. Based on our experiment, we show that our system is very accurate in generating a viable execution plan from a complex instruction.