DeLTa: Demonstration and Language-Guided Novel Transparent Object Manipulation
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915812751704064 |
|---|---|
| author | Lee, Taeyeop Kang, Gyuree Wen, Bowen Kim, Youngho Back, Seunghyeok Kweon, In So Shim, David Hyunchul Yoon, Kuk-Jin |
| author_facet | Lee, Taeyeop Kang, Gyuree Wen, Bowen Kim, Youngho Back, Seunghyeok Kweon, In So Shim, David Hyunchul Yoon, Kuk-Jin |
| contents | Despite the prevalence of transparent object interactions in human everyday life, transparent robotic manipulation research remains limited to short-horizon tasks and basic grasping capabilities. Although some methods have partially addressed these issues, most of them have limitations in generalization to novel objects and are insufficient for precise long-horizon robot manipulation. To address this limitation, we propose DeLTa (Demonstration and Language-Guided Novel Transparent Object Manipulation), a novel framework that integrates depth estimation, 6D pose estimation, and vision-language planning for precise long-horizon manipulation of transparent objects guided by natural language task instructions. A key advantage of our method is its single-demonstration approach, which generalizes 6D trajectories to novel transparent objects without requiring category-level priors or additional training. Additionally, we present a task planner that refines the VLM-generated plan to account for the constraints of a single-arm, eye-in-hand robot for long-horizon object manipulation tasks. Through comprehensive evaluation, we demonstrate that our method significantly outperforms existing transparent object manipulation approaches, particularly in long-horizon scenarios requiring precise manipulation capabilities. Project page: https://sites.google.com/view/DeLTa25/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_05662 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DeLTa: Demonstration and Language-Guided Novel Transparent Object Manipulation Lee, Taeyeop Kang, Gyuree Wen, Bowen Kim, Youngho Back, Seunghyeok Kweon, In So Shim, David Hyunchul Yoon, Kuk-Jin Robotics Computer Vision and Pattern Recognition Despite the prevalence of transparent object interactions in human everyday life, transparent robotic manipulation research remains limited to short-horizon tasks and basic grasping capabilities. Although some methods have partially addressed these issues, most of them have limitations in generalization to novel objects and are insufficient for precise long-horizon robot manipulation. To address this limitation, we propose DeLTa (Demonstration and Language-Guided Novel Transparent Object Manipulation), a novel framework that integrates depth estimation, 6D pose estimation, and vision-language planning for precise long-horizon manipulation of transparent objects guided by natural language task instructions. A key advantage of our method is its single-demonstration approach, which generalizes 6D trajectories to novel transparent objects without requiring category-level priors or additional training. Additionally, we present a task planner that refines the VLM-generated plan to account for the constraints of a single-arm, eye-in-hand robot for long-horizon object manipulation tasks. Through comprehensive evaluation, we demonstrate that our method significantly outperforms existing transparent object manipulation approaches, particularly in long-horizon scenarios requiring precise manipulation capabilities. Project page: https://sites.google.com/view/DeLTa25/ |
| title | DeLTa: Demonstration and Language-Guided Novel Transparent Object Manipulation |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.05662 |