Papers
39
Total Citations
1,133
H-Index
17
About
Ashwin Balakrishna is a leading researcher at the intersection of robotics, imitation learning, and large-scale robot manipulation. His work focuses on enabling robots to perform complex, real-world tasks—from fabric smoothing and folding to retrieving objects from cluttered bins—by combining deep learning with algorithmic supervision and model-based reinforcement learning. Balakrishna’s contributions are both foundational and widely cited: his paper on *Mechanical Search* (108 citations) introduced a multi-step retrieval framework for occluded objects, while his work on *Deep Imitation Learning of Sequential Fabric Smoothing* (109 citations) and *Learning Rope Manipulation Policies* (103 citations) advanced deformable object manipulation. He has also been instrumental in large-scale collaborative efforts, co-authoring *Open X-Embodiment* (119 citations) and *DROID* (108 citations), which provide massive datasets and models for generalist robotic learning. More recently, his work on *OpenVLA* (39 citations) pushes toward open-source vision-language-action models. With over 800 cumulative citations, Balakrishna’s research is shaping the future of adaptive, data-driven robotics—making him a key figure for students and researchers interested in scalable manipulation and embodied AI.
Research Focus
Key Achievements
Top Papers
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- 3Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter108 citations · 2019
- 4DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset108 citations · 2024
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- 7VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation71 citations · 2020
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- 9VisuoSpatial Foresight for physical sequential fabric manipulation41 citations · 2021
- 10OpenVLA: An Open-Source Vision-Language-Action Model39 citations · 2024