让机器人通过触觉+视觉+语言理解物体,还能边摸边学新东西。
Demonstrating the Octopi-1.5 Visual-Tactile-Language Model
- 用多部位触觉信号和检索增强生成,提升对物体的感知能力。
- 能实时识别被握物体并给出处理建议,如轻拿软果。
- 无需机械臂,用手持设备就能体验,适合研究者和开发者。
触觉是人类和机器人完成灵巧操作、材料识别及视觉遮挡场景中的关键感知模态。本文展示最新的视觉-触觉-语言模型Octopi-1.5,相比前代可处理多个物体部位的触觉信号,并引入简单检索增强生成(RAG)模块,提升任务表现并支持在运行中学习新物体。系统通过新型手持式触觉交互装置TMI(集成GelSight与TAC-02传感器)实现实时交互,用户无需机器人即可体验。演示中,Octopi-1.5将基于触觉输入和常识知识完成触觉推理任务,例如在猜物游戏中识别被握物体并回答后续问题(如建议轻拿软水果)。我们还将演示其RAG功能:即时教授模型新物品。该演示旨在展示视觉-触觉-语言模型(VTLMs)的进展与局限,激发领域兴趣。代码与TMI设计文件见https://github.com/clear-nus/octopi-1.5。
原文摘要 · Abstract (English)
Touch is recognized as a vital sense for humans and an equally important modality for robots, especially for dexterous manipulation, material identification, and scenarios involving visual occlusion. Building upon very recent work in touch foundation models, this demonstration will feature Octopi-1.5, our latest visual-tactile-language model. Compared to its predecessor, Octopi-1.5 introduces the ability to process tactile signals from multiple object parts and employs a simple retrieval-augmented generation (RAG) module to improve performance on tasks and potentially learn new objects on-the-fly. The system can be experienced live through a new handheld tactile-enabled interface, the TMI, equipped with GelSight and TAC-02 tactile sensors. This convenient and accessible setup allows users to interact with Octopi-1.5 without requiring a robot. During the demonstration, we will showcase Octopi-1.5 solving tactile inference tasks by leveraging tactile inputs and commonsense knowledge. For example, in a Guessing Game, Octopi-1.5 will identify objects being grasped and respond to follow-up queries about how to handle it (e.g., recommending careful handling for soft fruits). We also plan to demonstrate Octopi-1.5's RAG capabilities by teaching it new items. With live interactions, this demonstration aims to highlight both the progress and limitations of VTLMs such as Octopi-1.5 and to foster further interest in this exciting field. Code for Octopi-1.5 and design files for the TMI gripper are available at https://github.com/clear-nus/octopi-1.5.
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