1 code implementation • 15 Sep 2021 • Daniel Seita, Abhinav Gopal, Zhao Mandi, John Canny
Then, students learn by running either offline RL or by using teacher data in combination with a small amount of self-generated data.
no code implementations • 26 Oct 2021 • Zhao Mandi, Fangchen Liu, Kimin Lee, Pieter Abbeel
We then study the multi-task setting, where multi-task training is followed by (i) one-shot imitation on variations within the training tasks, (ii) one-shot imitation on new tasks, and (iii) fine-tuning on new tasks.
no code implementations • 7 Jun 2022 • Zhao Mandi, Pieter Abbeel, Stephen James
From these findings, we advocate for evaluating future meta-RL methods on more challenging tasks and including multi-task pretraining with fine-tuning as a simple, yet strong baseline.
no code implementations • 12 Dec 2022 • Zhao Mandi, Homanga Bharadhwaj, Vincent Moens, Shuran Song, Aravind Rajeswaran, Vikash Kumar
On a real robot setup, CACTI enables efficient training of a single policy that can perform 10 manipulation tasks involving kitchen objects, and is robust to varying layouts of distractors.
1 code implementation • 10 Jul 2023 • Zhao Mandi, Shreeya Jain, Shuran Song
We propose a novel approach to multi-robot collaboration that harnesses the power of pre-trained large language models (LLMs) for both high-level communication and low-level path planning.
no code implementations • 30 Nov 2023 • Bardienus P. Duisterhof, Zhao Mandi, Yunchao Yao, Jia-Wei Liu, Mike Zheng Shou, Shuran Song, Jeffrey Ichnowski
MD-Splatting builds on recent advances in Gaussian splatting, a method that learns the properties of a large number of Gaussians for state-of-the-art and fast novel view synthesis.