Search Results for author:
Found 14 papers, 7 papers with code
World Modeling Makes a Better Planner: Dual Preference Optimization for Embodied Task Planning
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.
BoT-Drive: Hierarchical Behavior and Trajectory Planning for Autonomous Driving using POMDPs
To enhance safety and robustness, the planner further applies importance sampling to refine the driving trajectory conditioned on the planned high-level behavior.
RI-MAE: Rotation-Invariant Masked AutoEncoders for Self-Supervised Point Cloud Representation Learning
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.
Rethinking State Disentanglement in Causal Reinforcement Learning
We revisit this research line and find that incorporating RL-specific context can reduce unnecessary assumptions in previous identifiability analyses for latent states.
Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey
Reinforcement Learning (RL) is a potent tool for sequential decision-making and has achieved performance surpassing human capabilities across many challenging real-world tasks.
The Planner Optimization Problem: Formulations and Frameworks
Identifying internal parameters for planning is crucial to maximizing the performance of a planner.
LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty
To address this, we propose a new algorithm, LEarning Attention over Driving bEhavioRs (LEADER), that learns to attend to critical human behaviors during planning.
Closing the Planning-Learning Loop with Application to Autonomous Driving
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
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.
SUMMIT: A Simulator for Urban Driving in Massive Mixed Traffic
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
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.
LeTS-Drive: Driving in a Crowd by Learning from Tree Search
LeTS-Drive leverages the robustness of planning and the runtime efficiency of learning to enhance the performance of both.
PORCA: Modeling and Planning for Autonomous Driving among Many Pedestrians
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
Planning under uncertainty is critical for robust robot performance in uncertain, dynamic environments, but it incurs high computational cost.
