Mrinal Kalakrishnan

Engineering · Physical Sciences

28h-index5.3kcitations62works13.82yr avg
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Research Topics

Robot Manipulation and Learning(38), Reinforcement Learning in Robotics(19), Muscle activation and electromyography studies(10), Robotic Locomotion and Control(7), Adversarial Robustness in Machine Learning(7)

Publications62 total

DexterityGen: Foundation Controller for Unprecedented Dexterity
2025· 3 citedOpen Access
DexterityGen: Foundation Controller for Unprecedented Dexterity
arXiv (Cornell University)·2025Open Access
NeuralFeels with neural fields: Visuotactile perception for in-hand manipulation
Science Robotics·2024· 47 cited
Sparsh: Self-supervised touch representations for vision-based tactile sensing
arXiv (Cornell University)·2024· 1 citedOpen Access
OpenEQA: Embodied Question Answering in the Era of Foundation Models
2024· 31 cited
What Do We Learn from a Large-Scale Study of Pre-Trained Visual Representations in Sim and Real Environments?
2024· 1 cited
Neural feels with neural fields: Visuo-tactile perception for in-hand manipulation
arXiv (Cornell University)·2023Open Access
Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots
arXiv (Cornell University)·2023· 15 citedOpen Access
What do we learn from a large-scale study of pre-trained visual representations in sim and real environments?
arXiv (Cornell University)·2023Open Access
USA-Net: Unified Semantic and Affordance Representations for Robot Memory
2023· 6 cited
Deep RL at Scale: Sorting Waste in Office Buildings with a Fleet of Mobile Manipulators
2023· 15 citedOpen Access
Deep RL at Scale: Sorting Waste in Office Buildings with a Fleet of Mobile Manipulators
arXiv (Cornell University)·2023· 1 citedOpen Access
USA-Net: Unified Semantic and Affordance Representations for Robot Memory
arXiv (Cornell University)·2023Open Access
How to train your robot with deep reinforcement learning: lessons we have learned
The International Journal of Robotics Research·2021· 519 citedOpen Access
Action Image Representation: Learning Scalable Deep Grasping Policies\n with Zero Real World Data
arXiv (Cornell University)·2020Open Access
Action Image Representation: Learning Scalable Deep Grasping Policies with Zero Real World Data
2020· 5 citedOpen Access
Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards
arXiv (Cornell University)·2020· 9 citedOpen Access
Watch, Try, Learn: Meta-Learning from Demonstrations and Reward
arXiv (Cornell University)·2019· 17 citedOpen Access
Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks
2019· 25 citedOpen Access
Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping
2019· 15 cited
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Frequent Co-authors

Peter Pástor(23), Stefan Schaal(21), Ludovic Righetti(13), Sergey Levine(11), Stefan Schaal(6), Julian Ibarz(6), Jonas Buchli(6), Michael Mistry(5), Adrian Li(5), Yunfei Bai(5), Daniel Kappler(5), Paul Wohlhart(5), Evangelos A. Theodorou(4), Alex Irpan(4), Alexander Herzog(4), Eric Jang(4), Yevgen Chebotar(4), Sachin Chitta(3), Dmitry Kalashnikov(3), Vincent Vanhoucke(3)