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Murtaza Dalal

@mihdalal • 33,779 subscribers

AI @ Tesla Optimus | Robotics/AI PhD @CMU_Robotics, EECS @UCBerkeley | Opinions entirely and unequivocally my own

Shorts

Imitation learning is powerful, but hard to scale due to lack of high quality data. Introducing Optimus: TAMP-supervised visuomotor transformers. Optimus solves over 300 long-horizon manipulation tasks with up to 8 stages and 72 different objects. NVIDIA AI Developer CMU Robotics Institute 1/N

Imitation learning is powerful, but hard to scale due to lack of high quality data. Introducing Optimus: TAMP-supervised visuomotor transformers. Optimus solves over 300 long-horizon manipulation tasks with up to 8 stages and 72 different objects. NVIDIA AI Developer CMU Robotics Institute 1/N

91,565 次观看

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Can my robot cook my food, rearrange my dresser, tidy my messy table and do so much more without ANY demos or real-world training data? Introducing ManipGen: A generalist agent for manipulation that can solve long-horizon robotics tasks entirely zero shot, from text input! 1/N

Can my robot cook my food, rearrange my dresser, tidy my messy table and do so much more without ANY demos or real-world training data? Introducing ManipGen: A generalist agent for manipulation that can solve long-horizon robotics tasks entirely zero shot, from text input! 1/N

149,222 次观看 • 1 年前

Can a single neural network policy generalize over poses, objects, obstacles, backgrounds, scene arrangements, in-hand objects, and start/goal states? Introducing Neural MP: A generalist policy for solving motion planning tasks in the real world 🤖 1/N

Can a single neural network policy generalize over poses, objects, obstacles, backgrounds, scene arrangements, in-hand objects, and start/goal states? Introducing Neural MP: A generalist policy for solving motion planning tasks in the real world 🤖 1/N

114,720 次观看 • 2 年前

LLMs are capable of high-level planning, but they require pre-trained skills! Our #ICLR2024 paper instead uses LLM guidance to train RL agents from scratch to solve 25+ long-horizon robotics tasks across four benchmarks w/ >85% success rates Paper & code:

LLMs are capable of high-level planning, but they require pre-trained skills! Our #ICLR2024 paper instead uses LLM guidance to train RL agents from scratch to solve 25+ long-horizon robotics tasks across four benchmarks w/ >85% success rates Paper & code:

36,308 次观看 • 2 年前

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