On a Connection between Differential Games, Optimal Control, and Energy-based Models for Multi-Agent Interactions
Game theory offers an interpretable mathematical framework for modeling multi-agent
interactions. However, its applicability in real-world robotics applications is hindered by …
interactions. However, its applicability in real-world robotics applications is hindered by …
[HTML][HTML] Autonomous driving in traffic with end-to-end vision-based deep learning
This paper presents a shallow end-to-end vision-based deep learning approach for
autonomous vehicle driving in traffic scenarios. The primary objectives include lane keeping …
autonomous vehicle driving in traffic scenarios. The primary objectives include lane keeping …
COIN: Chance-Constrained Imitation Learning for Uncertainty-aware Adaptive Resource Oversubscription Policy
We address the challenge of learning safe and robust decision policies in presence of
uncertainty in context of the real scientific problem of adaptive resource oversubscription to …
uncertainty in context of the real scientific problem of adaptive resource oversubscription to …
SOMTP: Self-Supervised Learning-Based Optimizer for MPC-Based Safe Trajectory Planning Problems in Robotics
Y Liu, Y Wang, G Li - arXiv preprint arXiv:2405.09212, 2024 - arxiv.org
Model Predictive Control (MPC)-based trajectory planning has been widely used in robotics,
and incorporating Control Barrier Function (CBF) constraints into MPC can greatly improve …
and incorporating Control Barrier Function (CBF) constraints into MPC can greatly improve …
LeTO: Learning Constrained Visuomotor Policy with Differentiable Trajectory Optimization
This paper introduces LeTO, a method for learning constrained visuomotor policy via
differentiable trajectory optimization. Our approach uniquely integrates a differentiable …
differentiable trajectory optimization. Our approach uniquely integrates a differentiable …