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Found 12 papers, 6 papers with code
Multi-Objective Trajectory Planning with Dual-Encoder
In this paper, we propose a two-stage approach to accelerate time-jerk optimal trajectory planning.
DecisionNCE: Embodied Multimodal Representations via Implicit Preference Learning
Multimodal pretraining has emerged as an effective strategy for the trinity of goals of representation learning in autonomous robots: 1) extracting both local and global task progression information; 2) enforcing temporal consistency of visual representation; 3) capturing trajectory-level language grounding.
A Comprehensive Survey of Cross-Domain Policy Transfer for Embodied Agents
Consequently, researchers often resort to data from easily accessible source domains, such as simulation and laboratory environments, for cost-effective data acquisition and rapid model iteration.
Stackelberg Driver Model for Continual Policy Improvement in Scenario-Based Closed-Loop Autonomous Driving
The deployment of autonomous vehicles (AVs) has faced hurdles due to the dominance of rare but critical corner cases within the long-tail distribution of driving scenarios, which negatively affects their overall performance.
Continual Driving Policy Optimization with Closed-Loop Individualized Curricula
To tackle this challenge, a surge of research in scenario-based autonomous driving has emerged, with a focus on generating high-risk driving scenarios and applying them to conduct safety-critical testing of AV models.
H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps
Solving real-world complex tasks using reinforcement learning (RL) without high-fidelity simulation environments or large amounts of offline data can be quite challenging.
(Re)$^2$H2O: Autonomous Driving Scenario Generation via Reversely Regularized Hybrid Offline-and-Online Reinforcement Learning
Autonomous driving and its widespread adoption have long held tremendous promise.
Discriminator-Guided Model-Based Offline Imitation Learning
In this paper, we propose the Discriminator-guided Model-based offline Imitation Learning (DMIL) framework, which introduces a discriminator to simultaneously distinguish the dynamics correctness and suboptimality of model rollout data against real expert demonstrations.
When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning
This brings up a new question: is it possible to combine learning from limited real data in offline RL and unrestricted exploration through imperfect simulators in online RL to address the drawbacks of both approaches?
A Versatile and Efficient Reinforcement Learning Framework for Autonomous Driving
Heated debates continue over the best autonomous driving framework.
DR2L: Surfacing Corner Cases to Robustify Autonomous Driving via Domain Randomization Reinforcement Learning
How to explore corner cases as efficiently and thoroughly as possible has long been one of the top concerns in the context of deep reinforcement learning (DeepRL) autonomous driving.
Tactical Decision Making for Emergency Vehicles Based on A Combinational Learning Method
The following approach reveals that DRL could complement rule-based avoiding strategy in generalization, and on the contrary, the rule-based avoiding strategy could complement DRL in stability, and their combination could lead to less response time, lower collision rate and smoother trajectory.
