mlsanityData to 7 Oct 2026

Demonstrating the Octopi-1.5 Visual-Tactile-Language Model

arXiv 2507.09985 ↗First posted 14 Jul 2025cs.RO cs.AIcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 13 of its 19 references that are in the map. First posted on arXiv 14 Jul 2025.

Builds on

13 of 19 references.

References · 13

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  4. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,151
  5. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  6. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  7. 2022Visual Prompt Tuningcited by 1,674
  8. 2024Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robotscited by 373
  9. 2020DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulationcited by 164
  10. 2024Binding Touch to Everything: Learning Unified Multimodal Tactile Representationscited by 94
  11. 2024A Touch, Vision, and Language Dataset for Multimodal Alignmentcited by 68
  12. 2021ObjectFolder: A Dataset of Objects with Implicit Visual, Auditory, and Tactile Representationscited by 67
1 more
  1. 2017Robotic Tactile Perception of Object Properties: A Reviewcited by 24

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 9.

9 papers.

Similar, not cited

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  1. 2024Octopi: Object Property Reasoning with Large Tactile-Language Models
  2. 2026Touch-R1: Reinforcing Touch Reasoning in MLLMs
  3. 2026SimpleTouch: Can Vision-Language-Action Models Master Contact-Rich Manipulation Without Tactile Policy Pretraining?
  4. 2025Tactile-VLA: Unlocking Vision-Language-Action Model's Physical Knowledge for Tactile Generalization
  5. 2026UniTacVLA: Unified Tactile Understanding and Prediction in Vision Language Action Models
  6. 2025VLA-Touch: Enhancing Vision-Language-Action Models with Dual-Level Tactile Feedback
  7. 2025OmniVTLA: Vision-Tactile-Language-Action Models with Semantic-Aligned Tactile Sensing
  8. 2026ContactWorld: What Representations Matter for Vision-Tactile Latent World Models in Contact-Rich Manipulation