# Multi-Scenario Reasoning: Unlocking Cognitive Autonomy in Humanoid Robots for Multimodal Understanding

arXiv: https://arxiv.org/abs/2412.20429 · HTML: https://mlsanity.com/paper/2412.20429/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 29 Dec 2024.

## Builds on (most cited first, 13 of 13)

- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Model-Based Reinforcement Learning via Meta-Policy Optimization](https://mlsanity.com/paper/1809.05214/) (2018)
- [Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing](https://mlsanity.com/paper/2312.01853/) (2023)
- [Sparser is Faster and Less is More: Efficient Sparse Attention for Long-Range Transformers](https://mlsanity.com/paper/2406.16747/) (2024)
- [Robot Learning in the Era of Foundation Models: A Survey](https://mlsanity.com/paper/2311.14379/) (2023)
- [World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges](https://mlsanity.com/paper/2301.05832/) (2023)
- [Sim-to-Real Learning for Humanoid Box Loco-Manipulation](https://mlsanity.com/paper/2310.03191/) (2023)
- [Does a Technique for Building Multimodal Representation Matter? -- Comparative Analysis](https://mlsanity.com/paper/2206.06367/) (2022)
- [LSG Attention: Extrapolation of pretrained Transformers to long sequences](https://mlsanity.com/paper/2210.15497/) (2022)
- [Cross-modality Data Augmentation for End-to-End Sign Language Translation](https://mlsanity.com/paper/2305.11096/) (2023)
- [Visuo-Haptic Object Perception for Robots: An Overview](https://mlsanity.com/paper/2203.11544/) (2022)
- [Hierarchical Reinforcement Learning Based on Planning Operators](https://mlsanity.com/paper/2309.14237/) (2023)
- [Multimodal Coherent Explanation Generation of Robot Failures](https://mlsanity.com/paper/2410.00659/) (2024)

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

- [Long Grounded Thoughts: Synthesizing Visual Problems and Reasoning Chains at Scale](https://mlsanity.com/paper/2511.05705/) (2025)

## Abstract (arXiv)

To improve the cognitive autonomy of humanoid robots, this research proposes a multi-scenario reasoning architecture to solve the technical shortcomings of multi-modal understanding in this field. It draws on simulation based experimental design that adopts multi-modal synthesis (visual, auditory, tactile) and builds a simulator "Maha" to perform the experiment. The findings demonstrate the feasibility of this architecture in multimodal data. It provides reference experience for the exploration of cross-modal interaction strategies for humanoid robots in dynamic environments. In addition, multi-scenario reasoning simulates the high-level reasoning mechanism of the human brain to humanoid robots at the cognitive level. This new concept promotes cross-scenario practical task transfer and semantic-driven action planning. It heralds the future development of self-learning and autonomous behavior of humanoid robots in changing scenarios.