3 papers
cs.RO2026
Real-World Reinforcement Learning with MPC Scaffolding for Dexterous Manipulation
Emek Barış Küçüktabak, Karankumar Patel, Zhaodong Yang +3
Real-world reinforcement learning (RL) offers a promising route to dexterous manipulation policies that can adapt directly from physical interaction, but learning is hindered by in…
cs.RO2026
Primitive-Informed Sampling-Based MPC for Multi-Fingered Dexterous Manipulation
Emek Barış Küçüktabak, Karankumar Patel, Jinda Cui +3
We present a primitive-informed sampling-based model predictive control (MPC) framework for multi-fingered dexterous manipulation. Sampling-based MPC avoids the need for gradients…
cs.RO2023
Virtual Physical Coupling of Two Lower-Limb Exoskeletons
Emek Barış Küçüktabak, Yue Wen, Matthew Short +3
Physical interaction between individuals plays an important role in human motor learning and performance during shared tasks. Using robotic devices, researchers have studied the ef…