activity
20182026
most citedINTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps

354 citations · 598 across the 53 of their papers we have counts for

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11 papers · 1 filter

cs.LG2025

SPACeR: Self-Play Anchoring with Centralized Reference Models

Wei-Jer Chang, Akshay Rangesh, Kevin Joseph +4

Developing autonomous vehicles (AVs) requires not only safety and efficiency, but also realistic, human-like behaviors that are socially aware and predictable. Achieving this requi…

cs.LG2025

LANGTRAJ: Diffusion Model and Dataset for Language-Conditioned Trajectory Simulation

Wei-Jer Chang, Wei Zhan, Masayoshi Tomizuka +2

Evaluating autonomous vehicles with controllability enables scalable testing in counterfactual or structured settings, enhancing both efficiency and safety. We introduce LangTraj,…

cs.LG2024

BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay

Catherine Weaver, Chen Tang, Ce Hao +3

Imitation learning learns a policy from demonstrations without requiring hand-designed reward functions. In many robotic tasks, such as autonomous racing, imitated policies must mo…

cs.LG2023

Residual Q-Learning: Offline and Online Policy Customization without Value

Chenran Li, Chen Tang, Haruki Nishimura +3

Imitation Learning (IL) is a widely used framework for learning imitative behavior from demonstrations. It is especially appealing for solving complex real-world tasks where handcr…

cs.LG2023

Skill-Critic: Refining Learned Skills for Hierarchical Reinforcement Learning

Ce Hao, Catherine Weaver, Chen Tang +3

Hierarchical reinforcement learning (RL) can accelerate long-horizon decision-making by temporally abstracting a policy into multiple levels. Promising results in sparse reward env…

cs.LG2023

Doubly Robust Self-Training

Banghua Zhu, Mingyu Ding, Philip Jacobson +4

Self-training is an important technique for solving semi-supervised learning problems. It leverages unlabeled data by generating pseudo-labels and combining them with a limited lab…