no code implementations • 25 May 2023 • Murtaza Dalal, Ajay Mandlekar, Caelan Garrett, Ankur Handa, Ruslan Salakhutdinov, Dieter Fox
In this work, we show that the combination of large-scale datasets generated by TAMP supervisors and flexible Transformer models to fit them is a powerful paradigm for robot manipulation.
no code implementations • NeurIPS 2021 • Devendra Singh Chaplot, Murtaza Dalal, Saurabh Gupta, Jitendra Malik, Ruslan Salakhutdinov
The observations gathered by this exploration policy are labelled using 3D consistency and used to improve the perception model.
no code implementations • NeurIPS 2021 • Murtaza Dalal, Deepak Pathak, Ruslan Salakhutdinov
An alternate but important component to consider improving is the interface of the RL algorithm with the robot.
6 code implementations • 16 Jun 2020 • Ashvin Nair, Abhishek Gupta, Murtaza Dalal, Sergey Levine
If we can instead allow RL algorithms to effectively use previously collected data to aid the online learning process, such applications could be made substantially more practical: the prior data would provide a starting point that mitigates challenges due to exploration and sample complexity, while the online training enables the agent to perfect the desired skill.
no code implementations • 25 Feb 2020 • Avi Singh, Eric Jang, Alexander Irpan, Daniel Kappler, Murtaza Dalal, Sergey Levine, Mohi Khansari, Chelsea Finn
In this work, we target this challenge, aiming to build an imitation learning system that can continuously improve through autonomous data collection, while simultaneously avoiding the explicit use of reinforcement learning, to maintain the stability, simplicity, and scalability of supervised imitation.
no code implementations • 17 Oct 2019 • Murtaza Dalal, Alexander C. Li, Rohan Taori
Autoregressive (AR) models have become a popular tool for unsupervised learning, achieving state-of-the-art log likelihood estimates.
2 code implementations • ICML 2020 • Vitchyr H. Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, Sergey Levine
Autonomous agents that must exhibit flexible and broad capabilities will need to be equipped with large repertoires of skills.
2 code implementations • NeurIPS 2018 • Ashvin Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, Sergey Levine
For an autonomous agent to fulfill a wide range of user-specified goals at test time, it must be able to learn broadly applicable and general-purpose skill repertoires.
1 code implementation • 19 Mar 2018 • Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou, Murtaza Dalal, Pieter Abbeel, Sergey Levine
Second, we show that policies learned with soft Q-learning can be composed to create new policies, and that the optimality of the resulting policy can be bounded in terms of the divergence between the composed policies.
no code implementations • ICLR 2018 • Vitchyr Pong, Shixiang Gu, Murtaza Dalal, Sergey Levine
TDMs combine the benefits of model-free and model-based RL: they leverage the rich information in state transitions to learn very efficiently, while still attaining asymptotic performance that exceeds that of direct model-based RL methods.