4 citations · 4 across the 2 of their papers we have counts for
4 papers · 1 filter
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…
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 evaluates candidate control tr…
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…
Haptic Transparency and Interaction Force Control for a Lower-Limb Exoskeleton
Emek Barış Küçüktabak, Yue Wen, Sangjoon J. Kim +6
Controlling the interaction forces between a human and an exoskeleton is crucial for providing transparency or adjusting assistance or resistance levels. However, it is an open pro…