1 citations · 1 across the 17 of their papers we have counts for
21 papers
Cross-Controller Transfer via In-Context Behavior Adaptation
Jonathan Yang, Chelsea Finn, Dorsa Sadigh
Generalist robot policies increasingly learn from data collected across diverse tasks and embodiments, yet typically assume a single controller for each robot. This assumption limi…
Generative Value Classifier
Alexander Swerdlow, Perry Dong, Chelsea Finn
Q-learning is premised on learning a function to predict the value of a state-action pair, with the goal of using such a function to guide policy improvement. Most approaches learn…
Mulligan: Performance-Guided Data Collection for Efficient On-Robot Learning
Lars Ankile, Perry Dong, Rohan Bhowmik +4
Learning from human demonstrations is a reliable way to teach robots new tasks, but the gains from each additional demonstration shrink as the policy improves. Continued improvemen…
Reinforcement Learning for Real-Time Vision-Language-Action Policies
Perry Dong, Kuo-Han Hung, Dorsa Sadigh +1
Reinforcement learning fine-tuning on top of large, pretrained Vision-Language-Action (VLA) models offers promise for highly reliable robot deployment. However, because of their sc…
Q-Learning With World Models
Perry Dong, Yueru Jia, Chelsea Finn +1
Off-policy reinforcement learning (RL) has become increasingly sample-efficient, enabling applications such as RL fine-tuning of Vision-Language-Action models into reliable, high-p…
Improving Robotic Generalist Policies via Flow Reversal Steering
Andy Tang, William Chen, Andrew Wagenmaker +2
Generalist policies can learn a wide range of skills from diverse robot datasets. In order to solve or improve on challenging new tasks, we need a way to infer and invoke the appro…
World Action Verifier: Self-Improving World Models via Forward-Inverse Asymmetry
Yuejiang Liu, Fan Feng, Lingjing Kong +6
General-purpose world models promise scalable policy evaluation, optimization, and planning, yet achieving the required level of robustness remains challenging. Unlike policy learn…
Poly-EPO: Training Exploratory Reasoning Models
Ifdita Hasan Orney, Jubayer Ibn Hamid, Shreya S Ramanujam +5
Exploration is a cornerstone of learning from experience: it enables agents to find solutions to complex problems, generalize to novel ones, and scale performance with test-time co…
EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models
Perry Dong, Kuo-Han Hung, Tian Gao +2
The ability to efficiently and reliably learn new tasks has been a foundational challenge in robotics. Vision-Language-Action (VLA) models have demonstrated strong generalization a…
FASTER: Value-Guided Sampling for Fast RL
Perry Dong, Alexander Swerdlow, Dorsa Sadigh +1
Some of the most performant reinforcement learning algorithms today can be prohibitively expensive as they use test-time scaling methods such as sampling multiple action candidates…
Data Analogies Enable Efficient Cross-Embodiment Transfer
Jonathan Yang, Chelsea Finn, Dorsa Sadigh
Generalist robot policies are trained on demonstrations collected across a wide variety of robots, scenes, and viewpoints. Yet it remains unclear how to best organize and scale suc…
TQL: Scaling Q-Functions with Transformers by Preventing Attention Collapse
Perry Dong, Kuo-Han Hung, Alexander Swerdlow +2
Despite scale driving substantial recent advancements in machine learning, reinforcement learning (RL) methods still primarily use small value functions. Naively scaling value func…
Invariance Co-training for Robot Visual Generalization
Jonathan Yang, Chelsea Finn, Dorsa Sadigh
Reasoning from diverse observations is a fundamental capability for generalist robot policies to operate in a wide range of environments. Despite recent advancements, many large-sc…
Value Flows
Perry Dong, Chongyi Zheng, Chelsea Finn +2
While most reinforcement learning methods today flatten the distribution of future returns to a single scalar value, distributional RL methods exploit the return distribution to pr…
Polychromic Objectives for Reinforcement Learning
Jubayer Ibn Hamid, Ifdita Hasan Orney, Ellen Xu +2
Reinforcement learning fine-tuning (RLFT) is a dominant paradigm for improving pretrained policies for downstream tasks. These pretrained policies, trained on large datasets, produ…
Self-Guided Action Diffusion
Rhea Malhotra, Yuejiang Liu, Chelsea Finn
Recent works have shown the promise of inference-time search over action samples for improving generative robot policies. In particular, optimizing cross-chunk coherence via bidire…
EXPO: Stable Reinforcement Learning with Expressive Policies
Perry Dong, Qiyang Li, Dorsa Sadigh +1
We study the problem of training and fine-tuning expressive policies with online reinforcement learning (RL) given an offline dataset. Training expressive policy classes with onlin…
Reinforcement Learning via Implicit Imitation Guidance
Perry Dong, Alec M. Lessing, Annie S. Chen +1
We study the problem of sample efficient reinforcement learning, where prior data such as demonstrations are provided for initialization in lieu of a dense reward signal. A natural…
Learning Long-Context Diffusion Policies via Past-Token Prediction
Marcel Torne, Andy Tang, Yuejiang Liu +1
Reasoning over long sequences of observations and actions is essential for many robotic tasks. Yet, learning effective long-context policies from demonstrations remains challenging…
What Matters for Batch Online Reinforcement Learning in Robotics?
Perry Dong, Suvir Mirchandani, Dorsa Sadigh +1
The ability to learn from large batches of autonomously collected data for policy improvement -- a paradigm we refer to as batch online reinforcement learning -- holds the promise…
Latent Diffusion Planning for Imitation Learning
Amber Xie, Oleh Rybkin, Dorsa Sadigh +1
Recent progress in imitation learning has been enabled by policy architectures that scale to complex visuomotor tasks, multimodal distributions, and large datasets. However, these…