Adaptive prediction ensemble: Improving out-of-distribution generalization of motion forecasting

J Li, J Li, S Bae, D Isele - IEEE Robotics and Automation …, 2024 - ieeexplore.ieee.org
Deep learning-based trajectory prediction models for autonomous driving often struggle with
generalization to out-of-distribution (OOD) scenarios, sometimes performing worse than …

Sonic: Safe social navigation with adaptive conformal inference and constrained reinforcement learning

J Yao, X Zhang, Y **a, Z Wang… - arxiv preprint arxiv …, 2024 - arxiv.org
Reinforcement Learning (RL) has enabled social robots to generate trajectories without
human-designed rules or interventions, which makes it more effective than hard-coded …

A Deep Reinforcement Learning Approach Using Asymmetric Self-Play for Robust Multirobot Flocking

Y Jia, Y Song, J Cheng, J **, W Zhang… - IEEE Transactions …, 2025 - ieeexplore.ieee.org
Flocking control, as an essential approach for survivable navigation of multirobot systems,
has been widely applied in fields, such as logistics, service delivery, and search and rescue …

Importance Sampling-Guided Meta-Training for Intelligent Agents in Highly Interactive Environments

M Arief, M Timmerman, J Li, D Isele… - IEEE Robotics and …, 2024 - ieeexplore.ieee.org
Training intelligent agents to navigate highly interactive environments presents significant
challenges. While guided meta reinforcement learning (RL) approach that first trains a …

Risk-Aware Autonomous Driving for Linear Temporal Logic Specifications

S Qi, Z Zhang, Z Sun, S Haesaert - arxiv preprint arxiv:2409.09769, 2024 - arxiv.org
Decision-making for autonomous driving incorporating different types of risks is a
challenging topic. This paper proposes a novel risk metric to facilitate the driving task …