In this paper, we introduce time-correlated model predictive path integral (TC-MPPI), a novel approach to mitigate action noise in sampling-based control methods. Unlike conventional smoothing techniques that rely on post-processing or additional state variables, TC-MPPI directly incorporates temporal correlation of actions into stochastic optimal control, effectively enforcing quadratic costs on action derivatives. This reformulation enables us to generate smooth action sequences without extra modifications, using a time-correlated and conditional Gaussian sampling distribution. We demonstrate the effectiveness of our approach through simulations on various robotic platforms, including a pendulum, cart-pole, 2D bicopter, 3D quadcopter, and autonomous vehicle. Simulation videos are available at https://youtu.be/nWfJ2MAV2JI.

Recent advances in Reinforcement Learning (RL) have demonstrated promising results in autonomous car racing. However, two fundamental challenges remain: sparse rewards, which hinder efficient learning process, and the quality of demonstrations, which directly affects the effectiveness of RL from Demonstration (RLfD) approaches. To address these issues, we propose SAC(λ), a novel RLfD algorithm tailored for sparse-reward racing tasks with imperfect demonstrations. SAC(λ) introduces two key components: (1) a discriminator-augmented Q-function, which integrates prior knowledge from demonstrations into value estimation while maintaining off-policy learning benefits, and (2) a Positive-Unlabeled (PU) learning framework with adaptive prior adjustment, which enables the agent to progressively refine its understanding of positive behaviors, while mitigating the overfitting problem. Through extensive experiments in the Assetto Corsa simulator, we demonstrate that SAC(λ) significantly accelerates training, surpasses the provided demonstrations, and achieves superior lap times over existing RL and RLfD approaches. Code and videos are available at https://heesungsung.github. io/AC-RLRacer/.

In this paper, we propose a real-time clothoid tree-based path planning for self-driving robots. Clothoids, curves that exhibit linear curvature profiles, play an important role in road design and path planning due to their appealing properties. Nevertheless, their real-time applications face considerable challenges, primarily stemming from the lack of a closed-form clothoid expression. To address these challenges, we introduce two innovative techniques: 1) an efficient and precise clothoid approximation using the Gauss-Legendre quadrature; and 2) a data-efficient decoder for interpolating clothoid splines that leverages the symmetry and similarity of clothoids. These techniques are demonstrated with numerical examples. The clothoid approximation ensures an accurate and smooth representation of the curve, and the clothoid spline decoder effectively accelerates the clothoid tree exploration by relaxing the problem constraints and reducing the problem size. Both techniques are integrated into our path planning algorithm and evaluated in various driving scenarios.