2 citations · 3 across the 6 of their papers we have counts for
8 papers
ExpertGen: Scalable Sim-to-Real Expert Policy Learning from Imperfect Behavior Priors
Zifan Xu, Ran Gong, Maria Vittoria Minniti +10
Learning generalizable and robust behavior cloning policies requires large volumes of high-quality robotics data. While human demonstrations (e.g., through teleoperation) serve as…
Adversarial Reinforcement Learning for Large Language Model Agent Safety
Zizhao Wang, Dingcheng Li, Vaishakh Keshava +4
Large Language Model (LLM) agents can leverage tools such as Google Search to complete complex tasks. However, this tool usage introduces the risk of indirect prompt injections, wh…
Dyn-O: Building Structured World Models with Object-Centric Representations
Zizhao Wang, Kaixin Wang, Li Zhao +2
World models aim to capture the dynamics of the environment, enabling agents to predict and plan for future states. In most scenarios of interest, the dynamics are highly centered…
Vision-based Manipulation from Single Human Video with Open-World Object Graphs
Yifeng Zhu, Arisrei Lim, Peter Stone +1
This work presents an object-centric approach to learning vision-based manipulation skills from human videos. We investigate the problem of robot manipulation via imitation in the…
Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination
Saad Abdul Ghani, Zizhao Wang, Peter Stone +1
This paper introduces Dynamic Learning from Learned Hallucination (Dyna-LfLH), a self-supervised method for training motion planners to navigate environments with dense and dynamic…
Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning
Zizhao Wang, Caroline Wang, Xuesu Xiao +2
Two desiderata of reinforcement learning (RL) algorithms are the ability to learn from relatively little experience and the ability to learn policies that generalize to a range of…
Learning Generalizable Manipulation Policies with Object-Centric 3D Representations
Yifeng Zhu, Zhenyu Jiang, Peter Stone +1
We introduce GROOT, an imitation learning method for learning robust policies with object-centric and 3D priors. GROOT builds policies that generalize beyond their initial training…
ELDEN: Exploration via Local Dependencies
Jiaheng Hu, Zizhao Wang, Peter Stone +1
Tasks with large state space and sparse rewards present a longstanding challenge to reinforcement learning. In these tasks, an agent needs to explore the state space efficiently un…