524 citations · 2.7k across the 55 of their papers we have counts for
20 papers · 1 filter
: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
Physical Intelligence, Bo Ai, Ali Amin +85
We present a new robotic foundation model, called , that can enable strong out-of-the-box performance in a wide range of scenarios. can follow diverse language i…
: a VLA That Learns From Experience
Physical Intelligence, Ali Amin, Raichelle Aniceto +53
We study how vision-language-action (VLA) models can improve through real-world deployments via reinforcement learning (RL). We present a general-purpose method, RL with Experience…
: a Vision-Language-Action Model with Open-World Generalization
Physical Intelligence, Kevin Black, Noah Brown +33
In order for robots to be useful, they must perform practically relevant tasks in the real world, outside of the lab. While vision-language-action (VLA) models have demonstrated im…
: A Vision-Language-Action Flow Model for General Robot Control
Kevin Black, Noah Brown, Danny Driess +21
Robot learning holds tremendous promise to unlock the full potential of flexible, general, and dexterous robot systems, as well as to address some of the deepest questions in artif…
Foundations for Transfer in Reinforcement Learning: A Taxonomy of Knowledge Modalities
Markus Wulfmeier, Arunkumar Byravan, Sarah Bechtle +2
Contemporary artificial intelligence systems exhibit rapidly growing abilities accompanied by the growth of required resources, expansive datasets and corresponding investments int…
PaLM-E: An Embodied Multimodal Language Model
Danny Driess, Fei Xia, Mehdi S. M. Sajjadi +19
Large language models excel at a wide range of complex tasks. However, enabling general inference in the real world, e.g., for robotics problems, raises the challenge of grounding.…
Offline Reinforcement Learning at Multiple Frequencies
Kaylee Burns, Tianhe Yu, Chelsea Finn +1
Leveraging many sources of offline robot data requires grappling with the heterogeneity of such data. In this paper, we focus on one particular aspect of heterogeneity: learning fr…
Jump-Start Reinforcement Learning
Ikechukwu Uchendu, Ted Xiao, Yao Lu +9
Reinforcement learning (RL) provides a theoretical framework for continuously improving an agent's behavior via trial and error. However, efficiently learning policies from scratch…
How to Leverage Unlabeled Data in Offline Reinforcement Learning
Tianhe Yu, Aviral Kumar, Yevgen Chebotar +3
Offline reinforcement learning (RL) can learn control policies from static datasets but, like standard RL methods, it requires reward annotations for every transition. In many case…
Autonomous Reinforcement Learning: Formalism and Benchmarking
Archit Sharma, Kelvin Xu, Nikhil Sardana +4
Reinforcement learning (RL) provides a naturalistic framing for learning through trial and error, which is appealing both because of its simplicity and effectiveness and because of…
Conservative Data Sharing for Multi-Task Offline Reinforcement Learning
Tianhe Yu, Aviral Kumar, Yevgen Chebotar +3
Offline reinforcement learning (RL) algorithms have shown promising results in domains where abundant pre-collected data is available. However, prior methods focus on solving indiv…
Autonomous Reinforcement Learning via Subgoal Curricula
Archit Sharma, Abhishek Gupta, Sergey Levine +2
Reinforcement learning (RL) promises to enable autonomous acquisition of complex behaviors for diverse agents. However, the success of current reinforcement learning algorithms is…
A Geometric Perspective on Self-Supervised Policy Adaptation
Cristian Bodnar, Karol Hausman, Gabriel Dulac-Arnold +1
One of the most challenging aspects of real-world reinforcement learning (RL) is the multitude of unpredictable and ever-changing distractions that could divert an agent from what…
Thinking While Moving: Deep Reinforcement Learning with Concurrent Control
Ted Xiao, Eric Jang, Dmitry Kalashnikov +4
We study reinforcement learning in settings where sampling an action from the policy must be done concurrently with the time evolution of the controlled system, such as when a robo…
Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning
Ryan Julian, Benjamin Swanson, Gaurav S. Sukhatme +3
One of the great promises of robot learning systems is that they will be able to learn from their mistakes and continuously adapt to ever-changing environments. Despite this potent…
Gradient Surgery for Multi-Task Learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta +3
While deep learning and deep reinforcement learning (RL) systems have demonstrated impressive results in domains such as image classification, game playing, and robotic control, da…
Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning
Abhishek Gupta, Vikash Kumar, Corey Lynch +2
We present relay policy learning, a method for imitation and reinforcement learning that can solve multi-stage, long-horizon robotic tasks. This general and universally-applicable,…
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He +7
Meta-reinforcement learning algorithms can enable robots to acquire new skills much more quickly, by leveraging prior experience to learn how to learn. However, much of the current…
Dynamics-Aware Unsupervised Discovery of Skills
Archit Sharma, Shixiang Gu, Sergey Levine +2
Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment. A good model can potentially enable planning algorithms…
Scaling simulation-to-real transfer by learning composable robot skills
Ryan Julian, Eric Heiden, Zhanpeng He +5
We present a novel solution to the problem of simulation-to-real transfer, which builds on recent advances in robot skill decomposition. Rather than focusing on minimizing the simu…