no code implementations • 2 Nov 2023 • Annie S. Chen, Govind Chada, Laura Smith, Archit Sharma, Zipeng Fu, Sergey Levine, Chelsea Finn
We provide theoretical analysis of our selection mechanism and demonstrate that ROAM enables a robot to adapt rapidly to changes in dynamics both in simulation and on a real Go1 quadruped, even successfully moving forward with roller skates on its feet.
no code implementations • 26 Oct 2023 • Laura Smith, YunHao Cao, Sergey Levine
Deep reinforcement learning (RL) can enable robots to autonomously acquire complex behaviors, such as legged locomotion.
no code implementations • 19 Apr 2023 • Laura Smith, J. Chase Kew, Tianyu Li, Linda Luu, Xue Bin Peng, Sehoon Ha, Jie Tan, Sergey Levine
Legged robots have enormous potential in their range of capabilities, from navigating unstructured terrains to high-speed running.
1 code implementation • 9 Apr 2023 • Kevin Zakka, Philipp Wu, Laura Smith, Nimrod Gileadi, Taylor Howell, Xue Bin Peng, Sumeet Singh, Yuval Tassa, Pete Florence, Andy Zeng, Pieter Abbeel
Replicating human-like dexterity in robot hands represents one of the largest open problems in robotics.
1 code implementation • 6 Feb 2023 • Philip J. Ball, Laura Smith, Ilya Kostrikov, Sergey Levine
Sample efficiency and exploration remain major challenges in online reinforcement learning (RL).
1 code implementation • 16 Aug 2022 • Laura Smith, Ilya Kostrikov, Sergey Levine
Deep reinforcement learning is a promising approach to learning policies in uncontrolled environments that do not require domain knowledge.
1 code implementation • 4 Nov 2021 • Kimin Lee, Laura Smith, Anca Dragan, Pieter Abbeel
However, it is difficult to quantify the progress in preference-based RL due to the lack of a commonly adopted benchmark.
1 code implementation • 8 Jul 2021 • Vitchyr H. Pong, Ashvin Nair, Laura Smith, Catherine Huang, Sergey Levine
If we can meta-train on offline data, then we can reuse the same static dataset, labeled once with rewards for different tasks, to meta-train policies that adapt to a variety of new tasks at meta-test time.
2 code implementations • 9 Jun 2021 • Kimin Lee, Laura Smith, Pieter Abbeel
We also show that our method is able to utilize real-time human feedback to effectively prevent reward exploitation and learn new behaviors that are difficult to specify with standard reward functions.
no code implementations • 10 Dec 2019 • Laura Smith, Nikita Dhawan, Marvin Zhang, Pieter Abbeel, Sergey Levine
In this paper, we study how these challenges can be alleviated with an automated robotic learning framework, in which multi-stage tasks are defined simply by providing videos of a human demonstrator and then learned autonomously by the robot from raw image observations.
no code implementations • EMNLP 2018 • Masoud Rouhizadeh, Kokil Jaidka, Laura Smith, H. Andrew Schwartz, Anneke Buffone, Lyle Ungar
Individuals express their locus of control, or {``}control{''}, in their language when they identify whether or not they are in control of their circumstances.
1 code implementation • ICLR 2019 • Marvin Zhang, Sharad Vikram, Laura Smith, Pieter Abbeel, Matthew J. Johnson, Sergey Levine
Model-based reinforcement learning (RL) has proven to be a data efficient approach for learning control tasks but is difficult to utilize in domains with complex observations such as images.
Model-based Reinforcement Learning reinforcement-learning +1