40 citations · 42 across the 6 of their papers we have counts for
7 papers
SIMLIFE: Pattern Understanding for Long-Horizon Human-Agent Partnership
Run Peng, Zinnia Nie, Jing Ding +7
Understanding humans over long horizons requires agents to infer not only what people need in the moment, but also how routines form, why they repeat, and when they change. We intr…
Ego2Web: A Web Agent Benchmark Grounded in Egocentric Videos
Shoubin Yu, Lei Shu, Antoine Yang +6
Multimodal AI agents are increasingly automating complex real-world workflows that involve online web execution. However, current web-agent benchmarks suffer from a critical limita…
Interactive and Expressive Code-Augmented Planning with Large Language Models
Anthony Z. Liu, Xinhe Wang, Jacob Sansom +5
Large Language Models (LLMs) demonstrate strong abilities in common-sense reasoning and interactive decision-making, but often struggle with complex, long-horizon planning tasks. R…
AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents
Yao Fu, Dong-Ki Kim, Jaekyeom Kim +4
Recent advances in large language models (LLMs) have empowered AI agents capable of performing various sequential decision-making tasks. However, effectively guiding LLMs to perfor…
Go Beyond Imagination: Maximizing Episodic Reachability with World Models
Yao Fu, Run Peng, Honglak Lee
Efficient exploration is a challenging topic in reinforcement learning, especially for sparse reward tasks. To deal with the reward sparsity, people commonly apply intrinsic reward…
Few-shot Subgoal Planning with Language Models
Lajanugen Logeswaran, Yao Fu, Moontae Lee +1
Pre-trained large language models have shown successful progress in many language understanding benchmarks. This work explores the capability of these models to predict actionable…
Model-Augmented Actor-Critic: Backpropagating through Paths
Ignasi Clavera, Violet Fu, Pieter Abbeel
Current model-based reinforcement learning approaches use the model simply as a learned black-box simulator to augment the data for policy optimization or value function learning.…