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Found 9 papers, 2 papers with code
Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving
However, maintaining safety in diverse safety-critical scenarios remains a significant challenge due to long-tailed and unforeseen scenarios absent from offline datasets.
Creative Robot Tool Use with Large Language Models
This paper investigates the feasibility of imbuing robots with the ability to creatively use tools in tasks that involve implicit physical constraints and long-term planning.
COMPOSER: Scalable and Robust Modular Policies for Snake Robots
Instead of perceiving the hyper-redundancy and flexibility of snake robots as mere challenges, there lies an unexplored potential in leveraging these traits to enhance robustness and generalizability at the control policy level.
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation.
GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning
In this work, we first formulate the problem of robotic water scooping using goal-conditioned reinforcement learning.
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables
One key challenge for multi-task Reinforcement learning (RL) in practice is the absence of task indicators.
Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory Prediction
To address these issues, we propose a novel approach to avoid KL vanishing and induce an interpretable interactive latent space with pseudo labels.
Learning Interpretable, High-Performing Policies for Autonomous Driving
While the performance of these approaches warrants real-world adoption, these policies lack interpretability, limiting deployability in the safety-critical and legally-regulated domain of autonomous driving (AD).
Active Hierarchical Imitation and Reinforcement Learning
Humans can leverage hierarchical structures to split a task into sub-tasks and solve problems efficiently.
