Dynamics-aware local trajectory control of a tracked vineyard robot via deep reinforcement learning

Waypoint Navigation of a 2D Drone in Stochastic Environment via Regularised Proximal Policy Optimisation

Autonomous navigation of aerial vehicles under stochastic wind disturbances is a challenging control problem that classical fixed‐gain methods struggle to handle reliably. We present a three‐stage training pipeline that combines imitation learning and reinforcement learning for 2D drone waypoint navigation: a cascaded PD expert collects quality‐filtered demonstration trajectories; behaviour cloning (BC) warm‐starts the policy by supervised imitation of those demonstrations; and Proximal Policy Optimization (PPO) fine‐tunes it through environment interaction, with an auxiliary BC loss retained at every update to prevent the RL gradient from erasing the imitation prior. Trained with randomly sampled targets, the policy generalises to unseen waypoints under continuously drifting wind, maintaining stable, near‐level flight. A systematic ablation across three random seeds shows that the strength of the auxiliary BC loss is the most critical design choice: removing it yields a policy that maximises shaped reward yet fails to navigate, crashing on nearly every episode, whereas over‐weighting it caps improvement near the demonstrator's level. Network capacity has negligible impact, whereas larger rollout buffers consistently yield the best and most consistent navigation. We further find that training reward and critic explained variance are unreliable proxies for navigation quality, underscoring the need to evaluate policies on held‐out targets.

Stochastic Multiple Shooting Trajectory Optimization via Sequential Local Policy Evaluation

Stochastic single shooting trajectory optimization methods such as Model Predictive Path Integral control (MPPI) have been widely adopted in robotics due to their ability to reason about probabilistic dynamics and provide solutions where model gradients are noisy, costly to evaluate, or unavailable. However, satisfaction of terminal constraints when shooting over long action sequences is often sample inefficient, requiring a large number of iterations for convergence. In this paper, we present a stochastic multiple shooting method that optimizes short control action sequences connected via local feedback policies to improve sample efficiency and convergence to a terminal set. Additionally, we show that we are able to synthesize approximate system Jacobians purely from rollouts, making the method suitable for model-based reinforcement learning with black-box dynamics. We demonstrate the algorithm has improved sample efficiency and terminal set convergence for three nonlinear, underactuated optimization problems: a classic cartpole swingup task with analytical dynamics, a cartpole swingup task with learned neural network dynamics, and a VTOL quadplane performing a high angle-of-attack, precision post-stall landing maneuver.

FARO: Feasibility-Aware Robot Motion Optimization

This paper proposes a nested kino-dynamic framework for rapid feasibility checking and dynamically consistent trajectory generation given a candidate contact sequence and shows that the generated trajectories can be tracked using a reinforcement learning (RL)-based controller and are of sufficiently high quality for execution in real-world loco-manipulation scenarios.

Deep reinforcement learning–based safe path planning for leader–follower robots

This work proposes a modified Multi-Agent Twin-Delayed Deep Deterministic Policy Gradient (M-MATD3) algorithm, specifically designed to mitigate common issues such as overestimation bias and high variance observed in standard MATD3.

Fast Adaptive Planning for Autonomous Tractor-Trailer Systems via Iterative LQR Steering

Physics-Aware End-to-End Deep Reinforcement Learning for Quadcopter Control with Actuator Dynamics

A physics-aware, end-to-end deep reinforcement learning (DRL) approach that acts directly on low-level body inputs, total thrust and body torques, and closes the loop through a high-fidelity Simulink environment is investigated.