307 citations · 626 across the 12 of their papers we have counts for
14 papers
Unlocking Generalization for Robotics via Modularity and Scale
Murtaza Dalal
How can we build generalist robot systems? Scale may not be enough due to the significant multimodality of robotics tasks, lack of easily accessible data and the challenges of depl…
Local Policies Enable Zero-shot Long-horizon Manipulation
Murtaza Dalal, Min Liu, Walter Talbott +4
Sim2real for robotic manipulation is difficult due to the challenges of simulating complex contacts and generating realistic task distributions. To tackle the latter problem, we in…
Neural MP: A Generalist Neural Motion Planner
Murtaza Dalal, Jiahui Yang, Russell Mendonca +3
The current paradigm for motion planning generates solutions from scratch for every new problem, which consumes significant amounts of time and computational resources. For complex…
Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks
Murtaza Dalal, Tarun Chiruvolu, Devendra Chaplot +1
Large Language Models (LLMs) have been shown to be capable of performing high-level planning for long-horizon robotics tasks, yet existing methods require access to a pre-defined s…
Imitating Task and Motion Planning with Visuomotor Transformers
Murtaza Dalal, Ajay Mandlekar, Caelan Garrett +3
Imitation learning is a powerful tool for training robot manipulation policies, allowing them to learn from expert demonstrations without manual programming or trial-and-error. How…
SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistency
Devendra Singh Chaplot, Murtaza Dalal, Saurabh Gupta +2
In this paper, we explore how we can build upon the data and models of Internet images and use them to adapt to robot vision without requiring any extra labels. We present a framew…
Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives
Murtaza Dalal, Deepak Pathak, Ruslan Salakhutdinov
Despite the potential of reinforcement learning (RL) for building general-purpose robotic systems, training RL agents to solve robotics tasks still remains challenging due to the d…
AWAC: Accelerating Online Reinforcement Learning with Offline Datasets
Ashvin Nair, Abhishek Gupta, Murtaza Dalal +1
Reinforcement learning (RL) provides an appealing formalism for learning control policies from experience. However, the classic active formulation of RL necessitates a lengthy acti…
Scalable Multi-Task Imitation Learning with Autonomous Improvement
Avi Singh, Eric Jang, Alexander Irpan +5
While robot learning has demonstrated promising results for enabling robots to automatically acquire new skills, a critical challenge in deploying learning-based systems is scale:…
Autoregressive Models: What Are They Good For?
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. We investigate the use of AR models as density…
Skew-Fit: State-Covering Self-Supervised Reinforcement Learning
Vitchyr H. Pong, Murtaza Dalal, Steven Lin +3
Autonomous agents that must exhibit flexible and broad capabilities will need to be equipped with large repertoires of skills. Defining each skill with a manually-designed reward f…
Visual Reinforcement Learning with Imagined Goals
Ashvin Nair, Vitchyr Pong, Murtaza Dalal +3
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. Furthermore…
Composable Deep Reinforcement Learning for Robotic Manipulation
Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou +3
Model-free deep reinforcement learning has been shown to exhibit good performance in domains ranging from video games to simulated robotic manipulation and locomotion. However, mod…
Temporal Difference Models: Model-Free Deep RL for Model-Based Control
Vitchyr Pong, Shixiang Gu, Murtaza Dalal +1
Model-free reinforcement learning (RL) is a powerful, general tool for learning complex behaviors. However, its sample efficiency is often impractically large for solving challengi…