6 papers
Robustness of Robotic Manipulation: Foundations and Frontiers
Yifei Dong, Zhanyi Sun, Lujie Yang +5
Humans and animals exhibit remarkable robustness in physical manipulation, yet robots remain far behind. Progress toward human-level manipulation robustness is hindered by the abse…
Latent Diffeomorphic Co-Design of End-Effectors for Deformable and Fragile Object Manipulation
Kei Ikemura, Yifei Dong, Florian T. Pokorny
Manipulating deformable and fragile objects remains a fundamental challenge in robotics due to complex contact dynamics and strict requirements on object integrity. Existing approa…
Sim-to-Real Gentle Manipulation of Deformable and Fragile Objects with Stress-Guided Reinforcement Learning
Kei Ikemura, Yifei Dong, David Blanco-Mulero +3
Robotic manipulation of deformable and fragile objects presents significant challenges, as excessive stress can lead to irreversible damage to the object. While existing solutions…
AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics
Yi Yang, Kei Ikemura, Qingwen Zhang +5
Recent multi-task learning studies suggest that linear scalarization, when using well-chosen fixed task weights, can achieve comparable to or even better performance than complex m…
Hard Cases Detection in Motion Prediction by Vision-Language Foundation Models
Yi Yang, Qingwen Zhang, Kei Ikemura +2
Addressing hard cases in autonomous driving, such as anomalous road users, extreme weather conditions, and complex traffic interactions, presents significant challenges. To ensure…
Robust Depth Enhancement via Polarization Prompt Fusion Tuning
Kei Ikemura, Yiming Huang, Felix Heide +3
Existing depth sensors are imperfect and may provide inaccurate depth values in challenging scenarios, such as in the presence of transparent or reflective objects. In this work, w…