Search Results for author: Tony Z. Zhao

Found 9 papers, 2 papers with code

Yell At Your Robot: Improving On-the-Fly from Language Corrections

no code implementations19 Mar 2024 Lucy Xiaoyang Shi, Zheyuan Hu, Tony Z. Zhao, Archit Sharma, Karl Pertsch, Jianlan Luo, Sergey Levine, Chelsea Finn

In this paper, we make the following observation: high-level policies that index into sufficiently rich and expressive low-level language-conditioned skills can be readily supervised with human feedback in the form of language corrections.

Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation

no code implementations4 Jan 2024 Zipeng Fu, Tony Z. Zhao, Chelsea Finn

We first present Mobile ALOHA, a low-cost and whole-body teleoperation system for data collection.

Imitation Learning

Waypoint-Based Imitation Learning for Robotic Manipulation

no code implementations26 Jul 2023 Lucy Xiaoyang Shi, Archit Sharma, Tony Z. Zhao, Chelsea Finn

AWE can be combined with any BC algorithm, and we find that AWE can increase the success rate of state-of-the-art algorithms by up to 25% in simulation and by 4-28% on real-world bimanual manipulation tasks, reducing the decision making horizon by up to a factor of 10.

Imitation Learning

Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

no code implementations23 Apr 2023 Tony Z. Zhao, Vikash Kumar, Sergey Levine, Chelsea Finn

Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously difficult for robots because they require precision, careful coordination of contact forces, and closed-loop visual feedback.

Chunking Imitation Learning

Calibrate Before Use: Improving Few-Shot Performance of Language Models

5 code implementations19 Feb 2021 Tony Z. Zhao, Eric Wallace, Shi Feng, Dan Klein, Sameer Singh

We show that this type of few-shot learning can be unstable: the choice of prompt format, training examples, and even the order of the training examples can cause accuracy to vary from near chance to near state-of-the-art.

Few-Shot Learning

MELD: Meta-Reinforcement Learning from Images via Latent State Models

1 code implementation26 Oct 2020 Tony Z. Zhao, Anusha Nagabandi, Kate Rakelly, Chelsea Finn, Sergey Levine

Meta-reinforcement learning algorithms can enable autonomous agents, such as robots, to quickly acquire new behaviors by leveraging prior experience in a set of related training tasks.

Meta-Learning Meta Reinforcement Learning +3

Concealed Data Poisoning Attacks on NLP Models

no code implementations NAACL 2021 Eric Wallace, Tony Z. Zhao, Shi Feng, Sameer Singh

In this work, we develop a new data poisoning attack that allows an adversary to control model predictions whenever a desired trigger phrase is present in the input.

Data Poisoning Language Modelling +2

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