Search Results for author: Panpan Cai

Found 14 papers, 7 papers with code

World Modeling Makes a Better Planner: Dual Preference Optimization for Embodied Task Planning

no code implementations13 Mar 2025 Siyin Wang, Zhaoye Fei, Qinyuan Cheng, Shiduo Zhang, Panpan Cai, Jinlan Fu, Xipeng Qiu

Recent advances in large vision-language models (LVLMs) have shown promise for embodied task planning, yet they struggle with fundamental challenges like dependency constraints and efficiency.

Task Planning

BoT-Drive: Hierarchical Behavior and Trajectory Planning for Autonomous Driving using POMDPs

no code implementations27 Sep 2024 Xuanjin Jin, Chendong Zeng, Shengfa Zhu, Chunxiao Liu, Panpan Cai

To enhance safety and robustness, the planner further applies importance sampling to refine the driving trajectory conditioned on the planned high-level behavior.

Autonomous Driving Decision Making +1

RI-MAE: Rotation-Invariant Masked AutoEncoders for Self-Supervised Point Cloud Representation Learning

1 code implementation31 Aug 2024 Kunming Su, Qiuxia Wu, Panpan Cai, Xiaogang Zhu, Xuequan Lu, Zhiyong Wang, Kun Hu

Finally, the predictor predicts the latent features of the masked patches using the output latent embeddings from the student, supervised by the outputs from the teacher.

Representation Learning Self-Supervised Learning

Rethinking State Disentanglement in Causal Reinforcement Learning

no code implementations24 Aug 2024 Haiyao Cao, Zhen Zhang, Panpan Cai, Yuhang Liu, Jinan Zou, Ehsan Abbasnejad, Biwei Huang, Mingming Gong, Anton Van Den Hengel, Javen Qinfeng Shi

We revisit this research line and find that incorporating RL-specific context can reduce unnecessary assumptions in previous identifiability analyses for latent states.

Disentanglement reinforcement-learning +2

Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey

2 code implementations19 Aug 2024 Ruiqi Zhang, Jing Hou, Florian Walter, Shangding Gu, Jiayi Guan, Florian Röhrbein, Yali Du, Panpan Cai, Guang Chen, Alois Knoll

Reinforcement Learning (RL) is a potent tool for sequential decision-making and has achieved performance surpassing human capabilities across many challenging real-world tasks.

Autonomous Driving Decision Making +6

The Planner Optimization Problem: Formulations and Frameworks

no code implementations12 Mar 2023 Yiyuan Lee, Katie Lee, Panpan Cai, David Hsu, Lydia E. Kavraki

Identifying internal parameters for planning is crucial to maximizing the performance of a planner.

LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty

1 code implementation23 Sep 2022 Mohamad H. Danesh, Panpan Cai, David Hsu

To address this, we propose a new algorithm, LEarning Attention over Driving bEhavioRs (LEADER), that learns to attend to critical human behaviors during planning.

Autonomous Driving

Closing the Planning-Learning Loop with Application to Autonomous Driving

no code implementations11 Jan 2021 Panpan Cai, David Hsu

To achieve real-time performance for large-scale planning, this work introduces a new algorithm Learning from Tree Search for Driving (LeTS-Drive), which integrates planning and learning in a closed loop, and applies it to autonomous driving in crowded urban traffic in simulation.

Autonomous Driving Robotics

MAGIC: Learning Macro-Actions for Online POMDP Planning

1 code implementation7 Nov 2020 Yiyuan Lee, Panpan Cai, David Hsu

The partially observable Markov decision process (POMDP) is a principled general framework for robot decision making under uncertainty, but POMDP planning suffers from high computational complexity, when long-term planning is required.

Computational Efficiency Decision Making +1

SUMMIT: A Simulator for Urban Driving in Massive Mixed Traffic

3 code implementations11 Nov 2019 Panpan Cai, Yiyuan Lee, Yuanfu Luo, David Hsu

Autonomous driving in an unregulated urban crowd is an outstanding challenge, especially, in the presence of many aggressive, high-speed traffic participants.

Robotics Multiagent Systems

GAMMA: A General Agent Motion Model for Autonomous Driving

1 code implementation4 Jun 2019 Yuanfu Luo, Panpan Cai, Yiyuan Lee, David Hsu

Further, the computational efficiency and the flexibility of GAMMA enable (i) simulation of mixed urban traffic at many locations worldwide and (ii) planning for autonomous driving in dense traffic with uncertain driver behaviors, both in real-time.

Autonomous Driving Collision Avoidance +3

LeTS-Drive: Driving in a Crowd by Learning from Tree Search

no code implementations29 May 2019 Panpan Cai, Yuanfu Luo, Aseem Saxena, David Hsu, Wee Sun Lee

LeTS-Drive leverages the robustness of planning and the runtime efficiency of learning to enhance the performance of both.

Autonomous Driving Imitation Learning

PORCA: Modeling and Planning for Autonomous Driving among Many Pedestrians

no code implementations30 May 2018 Yuanfu Luo, Panpan Cai, Aniket Bera, David Hsu, Wee Sun Lee, Dinesh Manocha

Our planning system combines a POMDP algorithm with the pedestrian motion model and runs in near real time.

Robotics

HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty

1 code implementation17 Feb 2018 Panpan Cai, Yuanfu Luo, David Hsu, Wee Sun Lee

Planning under uncertainty is critical for robust robot performance in uncertain, dynamic environments, but it incurs high computational cost.

Computational Efficiency

Cannot find the paper you are looking for? You can Submit a new open access paper.