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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About Me
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 1
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portfolio
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publications
Predicting 3d shapes, masks, and properties of materials, liquids, and objects inside transparent containers, using the Transproteus CGI dataset
Published in In Submission, 2021
Prediction of 3D shapes, masks, and properties of materials inside transparent containers using the Transproteus CGI dataset.
Recommended citation: Eppel, S., Xu, H., Wang, Y. R., & Aspuru-Guzik, A. (2021). Predicting 3d shapes, masks, and properties of materials, liquids, and objects inside transparent containers, using the Transproteus CGI dataset. In Submission. https://arxiv.org/abs/2109.07577
Seeing Glass: Joint Point-Cloud and Depth Completion for Transparent Objects
Published in 5th Annual Conference on Robot Learning (CoRL) - Oral Presentation (6.5% Acceptance Rate), 2021
Joint point-cloud and depth completion techniques for transparent object perception.
Recommended citation: Wang, Y. R.*, Xu, H.*, Eppel, S., Aspuru-Guzik, A., Shkurti, F., & Garg, A. (2021). Seeing Glass: Joint Point-Cloud and Depth Completion for Transparent Objects. 5th Annual Conference on Robot Learning (CoRL). https://arxiv.org/abs/2110.00087
CONetV2: Efficient Auto-Channel Size Optimization for CNNs
Published in International Conference on Machine Learning and Applications (ICMLA) - Oral Presentation, 2021
Efficient auto-channel size optimization techniques for convolutional neural networks.
Recommended citation: Wang, Y. R.*, Khaki, S.*, Zheng, W.*, Hosseini, M. S.*, & Plataniotis, K. N. (2021). CONetV2: Efficient Auto-Channel Size Optimization for CNNs. International Conference on Machine Learning and Applications (ICMLA). https://arxiv.org/abs/2110.06830
NEWTON: Are Large Language Models Capable of Physical Reasoning?
Published in Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023
Investigation of large language models capabilities for physical reasoning tasks.
Recommended citation: Wang, Y. R., Duan, J., Fox, D., & Srinivasa, S. (2023). NEWTON: Are Large Language Models Capable of Physical Reasoning? Conference on Empirical Methods in Natural Language Processing (EMNLP). https://arxiv.org/abs/2310.07018
AR2D2: Training a Robot Without A Robot
Published in Conference on Robot Learning (CoRL), 2023
A novel approach for training robotic systems without requiring physical robot hardware.
Recommended citation: Duan, J., Wang, Y. R., Shridhar, M., Fox, D., & Krishna, R. (2023). AR2D2: Training a Robot Without A Robot. Conference on Robot Learning (CoRL). https://arxiv.org/abs/2306.13818
MVTrans: Multi-View Perception for Transparent Objects
Published in IEEE International Conference on Robotics and Automation (ICRA), 2023
Multi-view perception techniques for transparent object detection and manipulation.
Recommended citation: Wang, Y. R.*, Zhao, Y.*, Xu, H.*, Aspuru-Guzik, A., Shkurti, F., & Garg, A. (2023). MVTrans: Multi-View Perception for Transparent Objects. IEEE International Conference on Robotics and Automation (ICRA). https://arxiv.org/abs/2302.11683
Manipulate-Anything: Automating Real-World Robots using Vision-Language Models
Published in Conference on Robot Learning (CoRL), 2024
Automation of real-world robots using vision-language models for manipulation tasks.
Recommended citation: Duan, J.*, Yuan, W.*, Pumacay, W., Wang, Y. R., Ehsani, K., Fox, D., & Krishna, R. (2024). Manipulate-Anything: Automating Real-World Robots using Vision-Language Models. Conference on Robot Learning (CoRL). https://arxiv.org/abs/2406.18915
MolmoAct: Action Reasoning Models that can Reason in Space
Published in arXiv preprint, 2025
Action reasoning models with spatial reasoning capabilities for robotic manipulation tasks.
Recommended citation: Lee, J., Duan, J., Fang, H., Deng, Y., Liu, S., Li, B., Fang, B., Zhang, J., Wang, Y. R., Lee, S., Han, W., Pumacay, W., Wu, A., Hendrix, R., Farley, K., VanderBilt, E., Farhadi, A., Fox, D., & Krishna, R. (2025). MolmoAct: Action Reasoning Models that can Reason in Space. arXiv preprint. https://arxiv.org/abs/2508.07917
PointArena: Probing Multimodal Grounding Through Language-Guided Pointing
Published in In Submission, 2025
A framework for probing multimodal grounding through language-guided pointing interactions.
Recommended citation: Cheng, L., Duan, J., Wang, Y. R., Fang, H., Li, B., Huang, Y., Wang, E., Eftekhar, A., Lee, J., Yuan, W., Hendrix, R., Smith, N. A., Xia, F., Fox, D., & Krishna, R. (2025). PointArena: Probing Multimodal Grounding Through Language-Guided Pointing. In Submission. https://arxiv.org/abs/2505.09990
SAM2Act: Integrating Visual Foundation Model with A Memory Architecture for Robotic Manipulation
Published in International Conference on Machine Learning (ICML), 2025
Integration of visual foundation models with memory architecture for enhanced robotic manipulation capabilities.
Recommended citation: Fang, H., Grotz, M., Pumacay, W., Wang, Y. R., Fox, D., Krishna, R., & Duan, J. (2025). SAM2Act: Integrating Visual Foundation Model with A Memory Architecture for Robotic Manipulation. International Conference on Machine Learning (ICML). https://arxiv.org/abs/2501.18564
AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation
Published in International Conference on Learning Representations (ICLR), 2025
A vision-language model for detecting and reasoning over failures in robotic manipulation tasks.
Recommended citation: Duan, J., Pumacay, W., Kumar, N., Wang, Y. R., Tian, S., Yuan, W., Krishna, R., Fox, D., Mandlekar, A., & Guo, Y. (2025). AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation. International Conference on Learning Representations (ICLR). https://arxiv.org/abs/2410.00371
RoboEval: Where Robotic Manipulation Meets Structured and Scalable Evaluation
Published in In Submission, 2025
A comprehensive evaluation framework for robotic manipulation tasks with structured and scalable assessment methods.
Recommended citation: Wang, Y. R., Ung, C., Tannert, G., Duan, J., Li, J., Le, A., Oswal, R., Grotz, M., Pumacay, W., Deng, Y., Krishna, R., Fox, D., & Srinivasa, S. (2025). RoboEval: Where Robotic Manipulation Meets Structured and Scalable Evaluation. In Submission. https://arxiv.org/abs/2507.00435
talks
Talk 1 on Relevant Topic in Your Field
Published:
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Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.