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Adaptive prediction ensemble: Improving out-of-distribution generalization of motion forecasting
Deep learning-based trajectory prediction models for autonomous driving often struggle with
generalization to out-of-distribution (OOD) scenarios, sometimes performing worse than …
generalization to out-of-distribution (OOD) scenarios, sometimes performing worse than …
Sonic: Safe social navigation with adaptive conformal inference and constrained reinforcement learning
Reinforcement Learning (RL) has enabled social robots to generate trajectories without
human-designed rules or interventions, which makes it more effective than hard-coded …
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
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 …
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
Training intelligent agents to navigate highly interactive environments presents significant
challenges. While guided meta reinforcement learning (RL) approach that first trains a …
challenges. While guided meta reinforcement learning (RL) approach that first trains a …
Risk-Aware Autonomous Driving for Linear Temporal Logic Specifications
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 …
challenging topic. This paper proposes a novel risk metric to facilitate the driving task …