Papers
21
Total Citations
363
H-Index
11
About
Iretiayo Akinola is a robotics researcher whose work spans robotic manipulation, multi-fingered grasping, sim-to-real transfer, and tactile sensing. He has made significant contributions to the challenge of contact-rich robotic assembly, most notably through the **Factory** framework (54 citations) and **IndustReal** (42 citations), which together establish a pipeline for training robots in simulation and deploying precise assembly skills in the real world. His early research tackled the notoriously difficult problem of multi-fingered grasping in cluttered environments, developing attention-driven reinforcement learning approaches in **Generative Attention Learning** (52 citations) and **Pixel-Attentive Policy Gradient** (39 citations) that significantly advanced dexterous manipulation capabilities. His work on adaptive tactile grasping using deep RL (31 citations) demonstrated how robots can recover from failed grasps using touch feedback — a problem vision alone cannot solve. More recently, Akinola developed **TacSL** (15 citations), a library for simulating visuotactile sensors, and contributed to fluid human-robot handovers and geometric motion planning frameworks. With over 300 cumulative citations across a focused body of work, his research consistently bridges the gap between simulation and real-world robotic dexterity, making him a compelling voice in next-generation manipulation research.
Research Focus
Key Achievements
Top Papers
- 1Factory: Fast Contact for Robotic Assembly54 citations · 2022
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- 7Model Predictive Control for Fluid Human-to-Robot Handovers17 citations · 2022
- 8TacSL: A Library for Visuotactile Sensor Simulation and Learning15 citations · 2025
- 9Task level hierarchical system for BCI-enabled shared autonomy14 citations · 2017
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