Utah Learning Lab for Manipulation Autonomy
LL4MA at University of Utah focuses on learning-based approaches to robotic manipulation autonomy. Their research addresses multi-object manipulation, latent space planning, and relational learning for complex manipulation tasks.
Notable achievements
IEEE Transactions on Robotics publications, latent space planning for multi-object manipulation
Notable work
Recent publications
All papers →Matched by this lab's specialties (keyword overlap + direct affiliation)
A hierarchical approach to imitation learning for manipulation tasks requiring time varying forces
Rishabh Shukla, Adithya Santhosh, Shaili Gandhi +2 more
Robotics and Computer-Integrated Manufacturing · 2026
What Are We Actually Benchmarking in Robot Manipulation?
Tianchong Jiang, Xiangshan Tan, Samuel Wheeler +3 more
2026
DLO-Lab: Benchmarking Deformable Linear Object Manipulations with Differentiable Physics
Junyi Cao, Yian Wang, Ziyan Xiong +3 more
2026
VLAConf: Calibrated Task-Success Confidence for Vision-Language-Action Models
Dehao Huang, Aoxiang Gu, Chengjie Zhang +5 more
2026
MiraBench: Evaluating Action-Conditioned Reliability in Robotic World Models
Tianzhuo Yang, Zihan Shen, Zirui Mi +7 more
2026
RealDexUMI: A Wearable Universal Manipulation Interface for Dexterous Robot Learning
Chaoyi Xu, Yixuan Jiang, Jiahui Huan +7 more
2026