Cristian C. Beltran-Hernandez
Papers
19
Total Citations
345
H-Index
8
About
Cristian C. Beltran-Hernandez is a robotics researcher whose work sits at the intersection of robot learning, contact-rich manipulation, and autonomous assembly. His research focuses primarily on reinforcement learning, imitation learning, and force control for robotic systems — areas where bridging the gap between simulation and real-world deployment remains one of the field's greatest challenges. His most influential contribution, "Learning Force Control for Contact-Rich Manipulation Tasks With Rigid Position-Controlled Robots" (2020, 150 citations), addressed a critical bottleneck in practical robotics: enabling stiff, position-controlled manipulators to handle delicate contact interactions through learned force control. This work has become a key reference for researchers tackling real-hardware RL deployment. He subsequently developed hybrid trajectory-and-force learning frameworks for complex assembly tasks, producing multiple well-cited studies that combine imitation learning with adaptive control strategies. More recently, Beltran-Hernandez has expanded into vision-language models for robot task planning, soft robotics with tactile sensing, and even food-slicing robots — demonstrating a broad and evolving research vision. With over 310 total citations and contributions spanning learning paradigms, hardware platforms, and application domains, his work offers valuable insights for students and practitioners pushing autonomous robots into real-world, contact-rich environments.
Research Focus
Key Achievements
Top Papers
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- 2Vision-Language Interpreter for Robot Task Planning35 citations · 2024
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- 9SliceIt! - A Dual Simulator Framework for Learning Robot Food Slicing8 citations · 2024
- 10Learning to Grasp with Primitive Shaped Object Policies8 citations · 2019