100 citations · 587 across the 34 of their papers we have counts for
8 papers · 1 filter
Foundation Models for Semantic Novelty in Reinforcement Learning
Tarun Gupta, Peter Karkus, Tong Che +2
Effectively exploring the environment is a key challenge in reinforcement learning (RL). We address this challenge by defining a novel intrinsic reward based on a foundation model,…
Guided Conditional Diffusion for Controllable Traffic Simulation
Ziyuan Zhong, Davis Rempe, Danfei Xu +5
Controllable and realistic traffic simulation is critical for developing and verifying autonomous vehicles. Typical heuristic-based traffic models offer flexible control to make ve…
LEAGUE: Guided Skill Learning and Abstraction for Long-Horizon Manipulation
Shuo Cheng, Danfei Xu
To assist with everyday human activities, robots must solve complex long-horizon tasks and generalize to new settings. Recent deep reinforcement learning (RL) methods show promise…
ProgPrompt: Generating Situated Robot Task Plans using Large Language Models
Ishika Singh, Valts Blukis, Arsalan Mousavian +6
Task planning can require defining myriad domain knowledge about the world in which a robot needs to act. To ameliorate that effort, large language models (LLMs) can be used to sco…
AdvDO: Realistic Adversarial Attacks for Trajectory Prediction
Yulong Cao, Chaowei Xiao, Anima Anandkumar +2
Trajectory prediction is essential for autonomous vehicles (AVs) to plan correct and safe driving behaviors. While many prior works aim to achieve higher prediction accuracy, few s…
BITS: Bi-level Imitation for Traffic Simulation
Danfei Xu, Yuxiao Chen, Boris Ivanovic +1
Simulation is the key to scaling up validation and verification for robotic systems such as autonomous vehicles. Despite advances in high-fidelity physics and sensor simulation, a…