Logically Constrained Robotics Transformers for Enhanced Perception-Action Planning
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866929455075688448 |
|---|---|
| author | Kapoor, Parv Vemprala, Sai Kapoor, Ashish |
| author_facet | Kapoor, Parv Vemprala, Sai Kapoor, Ashish |
| contents | With the advent of large foundation model based planning, there is a dire need to ensure their output aligns with the stakeholder's intent. When these models are deployed in the real world, the need for alignment is magnified due to the potential cost to life and infrastructure due to unexpected faliures. Temporal Logic specifications have long provided a way to constrain system behaviors and are a natural fit for these use cases. In this work, we propose a novel approach to factor in signal temporal logic specifications while using autoregressive transformer models for trajectory planning. We also provide a trajectory dataset for pretraining and evaluating foundation models. Our proposed technique acheives 74.3 % higher specification satisfaction over the baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05336 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Logically Constrained Robotics Transformers for Enhanced Perception-Action Planning Kapoor, Parv Vemprala, Sai Kapoor, Ashish Robotics Artificial Intelligence With the advent of large foundation model based planning, there is a dire need to ensure their output aligns with the stakeholder's intent. When these models are deployed in the real world, the need for alignment is magnified due to the potential cost to life and infrastructure due to unexpected faliures. Temporal Logic specifications have long provided a way to constrain system behaviors and are a natural fit for these use cases. In this work, we propose a novel approach to factor in signal temporal logic specifications while using autoregressive transformer models for trajectory planning. We also provide a trajectory dataset for pretraining and evaluating foundation models. Our proposed technique acheives 74.3 % higher specification satisfaction over the baselines. |
| title | Logically Constrained Robotics Transformers for Enhanced Perception-Action Planning |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2408.05336 |