Robotics Domain
Robotics research currently focuses on enhancing robot autonomy, safety, and dexterity, particularly in unstructured environments. Key areas include developing robust control algorithms (like Model Predictive Control and reinforcement learning), improving perception through advanced sensor fusion and generative models, and creating more efficient and adaptable robot designs. These advancements are driving progress in diverse applications such as agriculture, healthcare, and manufacturing, ultimately aiming to create more capable and reliable robots for a wider range of tasks.
Papers
Adaptable Recovery Behaviors in Robotics: A Behavior Trees and Motion Generators(BTMG) Approach for Failure Management
Faseeh Ahmad, Matthias Mayr, Sulthan Suresh-Fazeela, Volker Krueger
EVE: Enabling Anyone to Train Robots using Augmented Reality
Jun Wang, Chun-Cheng Chang, Jiafei Duan, Dieter Fox, Ranjay Krishna
Research Challenges for Adaptive Architecture: Empowering Occupants of Multi-Occupancy Buildings
Binh Vinh Duc Nguyen, Andrew Vande Moere
Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art
Neeloy Chakraborty, Melkior Ornik, Katherine Driggs-Campbell
Embedding Pose Graph, Enabling 3D Foundation Model Capabilities with a Compact Representation
Hugues Thomas, Jian Zhang
ManiPose: A Comprehensive Benchmark for Pose-aware Object Manipulation in Robotics
Qiaojun Yu, Ce Hao, Junbo Wang, Wenhai Liu, Liu Liu, Yao Mu, Yang You, Hengxu Yan, Cewu Lu
Robotics meets Fluid Dynamics: A Characterization of the Induced Airflow below a Quadrotor as a Turbulent Jet
Leonard Bauersfeld, Koen Muller, Dominic Ziegler, Filippo Coletti, Davide Scaramuzza