Kalakrishnan, Mrinal

QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation

Kalashnikov, D., Irpan, A., Pastor, P., Ibarz, J., Herzog, A., Jang, E., Quillen, D., Holly, E., Kalakrishnan, M., Vanhoucke, V., et al. (2018) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 697

Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping

Bousmalis, K., Irpan, A., Wohlhart, P., Bai, Y., Kelcey, M., Kalakrishnan, M., Downs, L., Ibarz, J., Pastor, P., Konolige, K., et al. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA)

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 636

How to train your robot with deep reinforcement learning: Lessons we have learned

Ibarz, J., Tan, J., Finn, C., Kalakrishnan, M., Pastor, P., & Levine, S. (2021) · The International Journal of Robotics Research

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 570

Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks

James, S., Wohlhart, P., Kalakrishnan, M., Kalashnikov, D., Irpan, A., Ibarz, J., Levine, S., Hadsell, R., & Bousmalis, K. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 420

Learning force control policies for compliant manipulation

Kalakrishnan, M., Righetti, L., Pastor, P., & Schaal, S. (2011) · 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 161

OpenEQA: Embodied Question Answering in the Era of Foundation Models

Majumdar, A., Ajay, A., Zhang, X., Putta, P., Yenamandra, S., Henaff, M., Silwal, S., Mcvay, P., Maksymets, O., Arnaud, S., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsRobot Manipulation and Learning

Cited by 101

Action Image Representation: Learning Scalable Deep Grasping Policies with Zero Real World Data

Khansari, M., Kappler, D., Luo, J., Bingham, J., & Kalakrishnan, M. (2020) · 2020 IEEE International Conference on Robotics and Automation (ICRA)

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 23

Habitat 3.0: A Co-Habitat for Humans, Avatars, and Robots

Puig, X., Undersander, E., Szot, A., Cote, M. D., Yang, T.-Y., Partsey, R., Desai, R., Clegg, A. W., Hlavac, M., Min, S. Y., et al. (2023) · arXiv

Robot Manipulation and LearningSocial Robot Interaction and HRI

Cited by 15

Sparsh: Self-supervised touch representations for vision-based tactile sensing

Higuera, C., Sharma, A., Bodduluri, C. K., Fan, T., Lancaster, P., Kalakrishnan, M., Kaess, M., Boots, B., Lambeta, M., Wu, T., et al. (2024) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 4