arXiv:2511. 06667v2 Announce Type: replace-cross Abstract: With the explosive growth of rigid-body simulators, policy learning in simulation has become the de facto standard for most rigid morphologies.
By Andrew Choi, Dezhong Tong, Xiaonan Huang
arXiv:2608. 00484v1 Announce Type: cross Abstract: Soft robotic substrates are promising for Physical Reservoir Computing (PRC) because their compliant nonlinear dynamics can provide temporal memory, high-dimensional state transformations, and efficient inference.
By Nicola Visentin, Maximilian St\"olzle, Mariano Ram\'irez Montero, Francesco Braghin, Daniela Rus, Cosimo Della Santina
arXiv:2506. 08630v3 Announce Type: replace Abstract: A universal controller for any robot morphology would greatly improve computational and data efficiency.
By Laurens Engwegen, Max Weltevrede, Caroline Horsch, Daan Brinks, Wendelin B\"ohmer
arXiv:2606. 05236v1 Announce Type: cross Abstract: Robotic arms capable of traversing arbitrary spatial paths, especially in highly obstructed workspaces, are highly desired across several industries.
By Tanapath Pornthisan, Thanapat Kemthong, Thanyapisit Kangsathien, Pasut Aranchaiya, Paulo Garcia, Viboon Sangveraphunsiri
arXiv:2606. 17394v1 Announce Type: cross Abstract: Adaptation to damages and in-situ physical repairs is essential for long-term robot autonomy, yet challenging outside of narrowly defined and well-anticipated bounds.
By James Avtges, Jake Ketchum, Helena Young, Taekyoung Kim, Ryan Truby, Todd Murphey
arXiv:2608. 06650v1 Announce Type: cross Abstract: Reduced-order models based on Cosserat-rod theory are now well established, and modeling theory is no longer the primary bottleneck in soft-robot control.
By Maximilian St\"olzle, Solange Gribonval, Daniel Feliu-Talegon, Vito Daniele Perfetta, Michele Martini, Chuhan Zhang, Kiwan Wong, Mohammed Tarnini, Anup Teejo Mathew, Federico Renda, Daniela Rus, Cosimo Della Santina
arXiv:2604. 09487v2 Announce Type: replace-cross Abstract: Tendon drives paired with soft muscle actuation enable faster and safer robots while potentially accelerating skill acquisition.
By Jan Schneider, Mridul Mahajan, Le Chen, Simon Guist, Bernhard Sch\"olkopf, Ingmar Posner, Dieter B\"uchler
arXiv:2606. 11525v1 Announce Type: cross Abstract: Contrastive Reinforcement Learning (CRL) has seen recent success in a wide variety of goal-conditioned robotics tasks by learning structured representations of the dynamics.
By Tongle Shen, Caleb Chuck, Fan Feng, Biwei Huang
arXiv:2606. 26183v1 Announce Type: cross Abstract: Building a generalist robot that can leverage prior knowledge for continuous task adaptation remains a significant challenge.
By Zhihao Gu, Lin Wang
arXiv:2607. 00272v1 Announce Type: cross Abstract: Traditional robot programming is challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse configurations and execution failures.
By Runyu Lu, Yubo Wu, Ethan Kou, Letian Fu, Wenli Xiao, Ajay Mandlekar, Yinzhen Xu, Guanya Shi, Ken Goldberg, Ang Chen, Mosharaf Chowdhury, Yuke Zhu, Linxi "Jim" Fan, Guanzhi Wang
arXiv:2510. 09497v2 Announce Type: replace-cross Abstract: In endovascular surgery, endovascular interventionists push a thin tube called a catheter, guided by a thin wire to a treatment site inside the patient's blood vessels to treat various conditions such as blood clots, aneurysms, and malformations.
By Noah Barnes, Ji Woong Kim, Lingyun Di, Hannah Qu, Anuruddha Bhattacharjee, Miroslaw Janowski, Dheeraj Gandhi, Bailey Felix, Shaopeng Jiang, Olivia Young, Mark Fuge, Ryan D. Sochol, Jeremy D. Brown, Axel Krieger
arXiv:2410. 24035v2 Announce Type: replace-cross Abstract: Generating robust and reactive manipulation strategies that can adapt to changing context information is a challenging task in robotics.
By Tim R. Winter, Leonard Kl\"upfel, Ashok M. Sundaram, Werner Friedl, Maximo A. Roa, Freek Stulp, Jo\~ao Silv\'erio