20 citations · 34 across the 18 of their papers we have counts for
9 papers · 1 filter
Prioritized Generative Replay
Renhao Wang, Kevin Frans, Pieter Abbeel +2
Sample-efficient online reinforcement learning often uses replay buffers to store experience for reuse when updating the value function. However, uniform replay is inefficient, sin…
Cliqueformer: Model-Based Optimization with Structured Transformers
Jakub Grudzien Kuba, Pieter Abbeel, Sergey Levine
Large neural networks excel at prediction tasks, but their application to design problems, such as protein engineering or materials discovery, requires solving offline model-based…
One Step Diffusion via Shortcut Models
Kevin Frans, Danijar Hafner, Sergey Levine +1
Diffusion models and flow-matching models have enabled generating diverse and realistic images by learning to transfer noise to data. However, sampling from these models involves i…
Offline Imitation Learning Through Graph Search and Retrieval
Zhao-Heng Yin, Pieter Abbeel
Imitation learning is a powerful machine learning algorithm for a robot to acquire manipulation skills. Nevertheless, many real-world manipulation tasks involve precise and dextero…
MOKA: Open-World Robotic Manipulation through Mark-Based Visual Prompting
Fangchen Liu, Kuan Fang, Pieter Abbeel +1
Open-world generalization requires robotic systems to have a profound understanding of the physical world and the user command to solve diverse and complex tasks. While the recent…
Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings
Kevin Frans, Seohong Park, Pieter Abbeel +1
Can we pre-train a generalist agent from a large amount of unlabeled offline trajectories such that it can be immediately adapted to any new downstream tasks in a zero-shot manner?…
Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control
Zhongyu Li, Xue Bin Peng, Pieter Abbeel +3
This paper presents a comprehensive study on using deep reinforcement learning (RL) to create dynamic locomotion controllers for bipedal robots. Going beyond focusing on a single l…
FMB: a Functional Manipulation Benchmark for Generalizable Robotic Learning
Jianlan Luo, Charles Xu, Fangchen Liu +5
In this paper, we propose a real-world benchmark for studying robotic learning in the context of functional manipulation: a robot needs to accomplish complex long-horizon behaviors…
Functional Graphical Models: Structure Enables Offline Data-Driven Optimization
Jakub Grudzien Kuba, Masatoshi Uehara, Pieter Abbeel +1
While machine learning models are typically trained to solve prediction problems, we might often want to use them for optimization problems. For example, given a dataset of protein…