Murtaza Dalal

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Hi! I am a Robotics PhD Candidate in the Robotics Institute at Carnegie Mellon University. I am advised by Professor Ruslan Salakhutdinov and I also work closely with Professor Deepak Pathak. My research work is supported by the National Science Foundation Graduate Research Fellowship. I am currently funded by and collaborating closely with Jian Zhang's group at Apple Research, where I work with Walter Talbott. During my PhD, I also spent a summer at NVIDIA research, where I worked in Professor Dieter Fox's lab, advised by Ankur Handa, Ajay Mandlekar and Caelan Garrett.

Previously, I was an undergraduate student at UC Berkeley where I received my BS in Electrical Engineering and Computer Science. I was an undergraduate researcher in Berkeley Artificial Intelligence and Research, working with Professor Sergey Levine, Vitchyr Pong and Ashvin Nair on deep reinforcement learning and robotics research. I also interned at (Google) X Robotics where I worked with Daniel Kappler.

Feel free to contact me via email! You can reach me at mdalal -at- andrew dot cmu dot edu

Research

I am interested in building generalist agents that are capable of solving any task of interest. To that end, my work focuses on unlocking generalization in robot learning by enabling agents to efficiently explore in the real world, leveraging hierarchy to quickly solve new tasks, performing imitation learning at scale to train powerful generalist agents and integrating large-scale learning into hierarchical systems to enable broadly capable manipulation systems. Here is some of my work (representative papers are highlighted):

	Neural MP: A Generalist Neural Motion Planner Murtaza Dalal, Jiahui Yang, Russell Mendonca, Youssef Khaky, Ruslan Salakhutdinov, Deepak Pathak arXiv \| site \| code \| video
	Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks Murtaza Dalal, Tarun Chiruvolu, Devendra Chaplot, Ruslan Salakhutdinov International Conference on Learning Representations (ICLR),* 2024 arXiv \| site \| code
	Imitating Task and Motion Planning with Visuomotor Transformers Murtaza Dalal, Ajay Mandlekar, Caelan Garrett, Ankur Handa, Ruslan Salakhutdinov, Dieter Fox Conference on Robot Learning (CoRL), 2023 arXiv \| site \| code
	SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistency Devendra Chaplot* Murtaza Dalal, Saurabh Gupta, Jitendra Malik, Ruslan Salakhutdinov Neural Information Processing Systems (NeurIPS), 2021 arXiv \| site \| video
	Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives Murtaza Dalal, Deepak Pathak, Ruslan Salakhutdinov Neural Information Processing Systems (NeurIPS),* 2021 arXiv \| site \| code
	Accelerating Online Reinforcement Learning with Offline Datasets Ashvin Nair, Abhishek Gupta, Murtaza Dalal, Sergey Levine arXiv \| site \| code \| blog
	Skew-Fit: State-Covering Self-Supervised Reinforcement Learning Vitchyr H. Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, Sergey Levine International Conference on Machine Learning (ICML),* 2020 arXiv \| site \| code
	Scalable Multi-Task Imitation Learning with Autonomous Improvement Avi Singh, Eric Jang, Alexander Irpan, Daniel Kappler, Murtaza Dalal, Sergey Levine, Mohi Khansari, Chelsea Finn International Conference on Robotics and Automation (ICRA), 2020.* arXiv \| site \| video
	Visual Reinforcement Learning with Imagined Goals Ashvin Nair, Vitchyr H. Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, Sergey Levine Advances in Neural Information Processing Systems (NeurIPS), 2018 (Spotlight) arXiv \| site \| code \| blog
	Composable Deep Reinforcement Learning for Robotic Manipulation Tuomas Haarnoja, Vitchyr H. Pong, Aurick Zhou, Murtaza Dalal, Pieter Abbeel, Sergey Levine International Conference on Robotics and Automation (ICRA), 2018.* arXiv \| site \| code
	Temporal Difference Models: Model-Free Deep RL for Model-Based Control Vitchyr H. Pong, Shixiang Gu, Murtaza Dalal, Sergey Levine International Conference on Learning Representations (ICLR), 2018. arXiv \| code \| blog

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