GT@ICRA 2025 Accepted Papers & Workshops

MI-HGNN: Morphology-Informed Heterogeneous Graph Neural Network for Legged Robot Contact Perception | Daniel Butterfield, Sandilya Sai Garimella, Nai-Jen Cheng, and Lu Gan 
Residual Descent Differential Dynamic Game (RD3G) -- A Fast Newton Solver for Constrained General Sum Games | Zhiyuan Zhang and Panagiotis Tsiotras
RAIL: Reachability-Aided Imitation Learning for Safe Policy Execution | Wonsuhk Jung, Dennis Anthony, Utkarsh A. Mishra, Nadun Ranawaka Arachchige, Matthew Bronars, Danfei Xu and  Shreyas Kousik
Guaranteed Reach-Avoid for Black-Box Systems through Narrow Gaps via Neural Network Reachability | Long Kiu Chung, Wonsuhk Jung, Srivatsank Pullabhotla, Parth Shinde, Yadu Sunil, Saihari Kota, Luis Felipe Wolf Batista, Cédric Pradalier, and Shreyas Kousik
Towards Closing the Loop in Robotic Pollination for Indoor Farming via Autonomous Microscopic Inspection | Chuizheng Kong, Alex Qiu, Idris Wibowo, Marvin Ren, Aishik Dhori, Kai-Shu Ling, Ai-Ping Hu, and Shreyas Kousik
AquaMILR: Mechanical Intelligence Simplifies Control of Undulatory Robots in Cluttered Fluid Environments | Tianyu Wang, Nishanth Mankame, Matthew Fernandez, Velin Kojouharov, Daniel I. Goldman
AquaMILR+: Design of An Untethered Limbless Robot for Complex Aquatic Terrain Navigation | Matthew Fernandez, Tianyu Wang, Galen Tunnicliffe, Donoven Dortilus, Peter Gunnarson, John O. Dabiri, Daniel I. Goldman
Addition of a Peristaltic Wave Improves Multi-Legged Locomotion Performance on Complex Terrains | Massimiliano Iaschi, Baxi Chong, Tianyu Wang, Jianfeng Lin, Juntao He, Daniel Soto, Zhaochen Xu, Daniel I Goldman
Effective Self-Righting Strategies for Elongate Multi-Legged Robots | Erik Teder, Baxi Chong, Juntao He, Tianyu Wang, Massimiliano Iaschi, Daniel Soto, Daniel I Goldman
CLIMB: Language-Guided Continual Learning for Task Planning with Iterative Model Building | Walker Byrnes, Miroslav Bogdanovic, Avi Balakirsky, Stephen Balakirsky, Animesh Garg
CE-MRS: Contrastive Explanations for Multi-Robot Systems | Ethan Schneider, Daniel Wu, Devleena Das, Sonia Chernova
 

Workshop on Foundation Models and Neural-Symbolic AI for Robotics
This workshop will address this emerging area by bringing together leading researchers and practitioners in robotics to share insights into the latest advancements, methodologies, and best practices. It will feature invited talks from academic and industrial leaders and technical paper presentations discussing groundbreaking approaches and robotic systems that leverage foundation models and neuro-symbolic AI. The workshop will also explore current challenges, future research directions, and promote interaction through poster sessions and spotlight talks.

Towards Agility and Robustness: Mechanical Intelligence in Robotics, Biology, and Smart Materials
This workshop focuses on advancing the concepts of agility and robustness in robotic locomotion and manipulation, incorporating innovations in mechanical intelligence (MI). Although robotics has achieved remarkable progress, reaching the level of agility and adaptability seen in nature remains a challenge. To tackle this, we aim to foster interdisciplinary discussions involving experts from robotics, biomechanics, physics, and materials science, encouraging fresh perspectives on how MI can improve robot performance in unstructured environments. By bringing together contributors from diverse fields, the workshop will reflect the state-of-the-art in MI, not only within varied robotic systems but also through insights gained from biological systems that demonstrate natural forms of agility and robustness. By understanding principles of how biological organisms achieve complex behaviors in dynamic and unpredictable environments, we can transfer these principles to robotics. This fusion of knowledge will allow us to explore how MI, through developing mechanisms of robot design and interaction with the environment, can enhance the capabilities of robots, pushing them closer to the adaptability and efficiency seen in nature.