Your Next Robot Might Think with Its Fingers

Authors: (1) Samson Yu, Dept. of Computer Science, National University of Singapore (samson.yu@u.nus.edu); (2) Kelvin Lin. Dept. of Computer Science, National University of Singapore; (3) Anxing Xiao, Dept. of Computer Science, National University of Singapore; (4) Jiafei Duan, University of Washington; (5) Harold Soh, Dept. of Computer Science, National University of Singapore and NUS Smart Systems Institute (harold@comp.nus.edu.sg). Authors: Authors: (1) Samson Yu, Dept. of Computer Science, National University of Singapore (samson.yu@u.nus.edu); (2) Kelvin Lin. Dept. of Computer Science, National University of Singapore; (3) Anxing Xiao, Dept. of Computer Science, National University of Singapore; (4) Jiafei Duan, University of Washington; (5) Harold Soh, Dept. of Computer Science, National University of Singapore and NUS Smart Systems Institute (harold@comp.nus.edu.sg). Table of Links Abstract and I. Introduction II. Related Work III. PhysiClear - Tactile and Physical Understanding Training & Evaluation Suite IV. Octopi - Vision-Language Property-Guided Physical Reasoning V. Experimental Setup VI. Experimental Results VII. Ablations VIII. Conclusion and Discussion, Acknowledgements, and References Appendix for Octopi: Object Property Reasoning with Large Tactile-Language Models APPENDIX A: ANNOTATION DETAILS APPENDIX B: OBJECT DETAILS APPENDIX C: PROPERTY STATISTICS APPENDIX D: SAMPLE VIDEO STATISTICS APPENDIX E: ENCODER ANALYSIS APPENDIX F: PG-INSTRUCTBLIP AVOCADO PROPERTY PREDICTION Abstract and I. Introduction Abstract and I. Introduction II. Related Work II. Related Work III. PhysiClear - Tactile and Physical Understanding Training & Evaluation Suite III. PhysiClear - Tactile and Physical Understanding Training & Evaluation Suite IV. Octopi - Vision-Language Property-Guided Physical Reasoning IV. Octopi - Vision-Language Property-Guided Physical Reasoning V. Experimental Setup V. Experimental Setup VI. Experimental Results VI. Experimental Results VII. Ablations VII. Ablations VIII. Conclusion and Discussion, Acknowledgements, and References Appendix for Octopi: Object Property Reasoning with Large Tactile-Language Models APPENDIX A: ANNOTATION DETAILS APPENDIX B: OBJECT DETAILS APPENDIX C: PROPERTY STATISTICS APPENDIX D: SAMPLE VIDEO STATISTICS APPENDIX E: ENCODER ANALYSIS APPENDIX F: PG-INSTRUCTBLIP AVOCADO PROPERTY PREDICTION VIII. Conclusion and Discussion, Acknowledgements, and References Appendix for Octopi: Object Property Reasoning with Large Tactile-Language Models APPENDIX A: ANNOTATION DETAILS APPENDIX B: OBJECT DETAILS APPENDIX C: PROPERTY STATISTICS APPENDIX D: SAMPLE VIDEO STATISTICS APPENDIX E: ENCODER ANALYSIS APPENDIX F: PG-INSTRUCTBLIP AVOCADO PROPERTY PREDICTION Appendix for Octopi: Object Property Reasoning with Large Tactile-Language Models APPENDIX A: ANNOTATION DETAILS APPENDIX B: OBJECT DETAILS APPENDIX C: PROPERTY STATISTICS APPENDIX D: SAMPLE VIDEO STATISTICS APPENDIX E: ENCODER ANALYSIS APPENDIX F: PG-INSTRUCTBLIP AVOCADO PROPERTY PREDICTION Appendix for Octopi: Object Property Reasoning with Large Tactile-Language Models APPENDIX A: ANNOTATION DETAILS APPENDIX B: OBJECT DETAILS APPENDIX C: PROPERTY STATISTICS APPENDIX D: SAMPLE VIDEO STATISTICS APPENDIX E: ENCODER ANALYSIS APPENDIX F: PG-INSTRUCTBLIP AVOCADO PROPERTY PREDICTION APPENDIX A: ANNOTATION DETAILS APPENDIX B: OBJECT DETAILS APPENDIX C: PROPERTY STATISTICS APPENDIX D: SAMPLE VIDEO STATISTICS APPENDIX E: ENCODER ANALYSIS APPENDIX F: PG-INSTRUCTBLIP AVOCADO PROPERTY PREDICTION VIII. CONCLUSION AND DISCUSSION In this work, we extended large vision-language models (LVLMs) to process and describe tactile inputs using physical properties. We proposed a tactile dataset called PHYSICLEAR, comprising data from vision (Camera) and tactile (GelSight) sensors collected from everyday objects, along with physical property annotations. We also present OCTOPI, a large tactile-language model trained using datasets like PHYSICLEAR to perform physical property reasoning using tactile inputs. Our experiments show that OCTOPI is able to describe tactile signals from novel unseen objects and that inferred physical properties can be used for physical reasoning and robot task completion in scenarios with visual ambiguity. We studied the impact of different components in OCTOPI, and found that using a task-specific visual encoder that is fine-tuned on our labels improves performance significantly across all tasks. This suggests that improvements to the visual encoder will yield benefits. In addition, parameter-efficient LLM fine-tuning consistently improved performance. Our work opens up future work in tactile robotics. We are currently working on tactile encoder improvement and more diverse exploratory procedures to obtain additional physical properties. It would also be interesting to combine different datasets (e.g., those using other tactile sensors [44, 45]), along with other modalities such as robot proprioception. We plan to also perform physical understanding alignment with object images and LLM fine-tuning with additional physical understanding data [52, 31]. ACKNOWLEDGEMENTS This research is supported by the National Research Foundation, Singapore under its Medium Sized Center for Advanced Robotics Technology Innovation. 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While the hardness and roughness properties are generally annotated with actual haptic feedback from human exploratory procedures, the bumpiness property is generally annotated visually from the GelSight tactile images. APPENDIX B: OBJECT DETAILS In Tables A3 and A4, we provide the list of objects in PHYSICLEAR, their associated video name, and the set (i.e. train/val/test). We use the object names in the left column during the Property-object Matching task. In Table A5, we list objects that have semantically meaningful parts for part-based physical reasoning tasks. Specifically, we note that their semantic parts can be used for more precise Property-object Matching. Surface materials of PHYSICLEAR objects are listed in Table A6. PHYSICLEAR contains nine different common materials: fabric, food, leather, metal, paper, plastic, rubber, silicone and wood. While we do not use these materials for any task currently, we catalog them for future extensions (e.g. material classification using properties). APPENDIX C: PROPERTY STATISTICS We show the object count and percentage of each property category in Table A7. There is at least 18.92% of each category for each property such that the dataset is sufficiently balanced. Table A8 shows the count for each combination of the three properties: hardness, roughness and bumpiness. The joint distribution of these properties across everyday objects is not uniform. Some combinations are much more common (e.g. [Hard, smooth, no bumps] for human-made objects) than others (e.g., [Soft, rough, no bumps]). Furthermore, the curse of dimensionality means that it is difficult to represent all combinations well as the number of properties increases. Our future work will incorporate better representation learning methods to handle this imbalance. APPENDIX D: SAMPLE VIDEO STATISTICS There are five to seven tactile videos for each object in PHYSICLEAR. Table A9 shows the average, minimum and maximum number of frames in our tactile videos at 112.30, 50 and 126 respectively. A. Language Prompt Details A. Language Prompt Details We list the questions for each task in this section. APPENDIX E: ENCODER ANALYSIS Fig. 6 visualizes the confusion matrices for the finetuned CLIP classifier’s predictions and Fig. 7 plots its visual encoder’s output embeddings for all unseen objects (dimensionality-reduced). We observe that the CLIP classifier performs well on Hard objects but not as well on the Soft and Moderately hard objects. This is also evident in the encoder embeddings in the leftmost part of Fig. 7 where the embeddings for the Hard objects are well-separated while the ones for Moderately hard objects are especially not well-separated. For the roughness property, the CLIP classifier performs well on Smooth and Slightly rough objects but not as well on the Rough objects. We can see this in the encoder embeddings in Fig. 7 where some of the embeddings for the Rough objects are interspersed with the embeddings for the Smooth objects. Lastly, for the bumpiness property, the CLIP is able to discriminate No bumps and Small bumps but not as well on objects with Big bumps. In the encoder embeddings at the right side of Fig. 7, the embeddings for the objects with Big bumps are interspersed with the embeddings for the objects with No bumps and Small bumps. APPENDIX F: PG-INSTRUCTBLIP AVOCADO PROPERTY PREDICTION For the avocado experiments with PG-InstructBLIP, we take 20 RGB images of each avocado from a top-down view on a flat gray surface. We vary the position and orientation of each avocado for each image and use cropped images (example shown in Fig. 8); the cropping of the avocado images makes it closer to the dataset PG-InstructBLIP was trained on. We use the original prompt provided in PG-InstructBLIP’s example script and prompt the model for each physical property: • Hardness - “Classify the object as hard, soft or medium? Respond unknown if you are not sure. Short answer:” • Roughness - “Classify the object as rough, smooth or medium? Respond unknown if you are not sure. Short answer:” • Bumpiness - “Classify the object as having big bumps, small bumps or no bumps? Respond unknown if you are not sure. Short answer:” PG-InstructBLIP is not trained on our three physical properties and we find that it never chooses “moderately hard” and “slightly rough”. Hence, we change those categories to “medium” for hardness and roughness. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv