13 accepted papers
In cluttered environments where rigid and deformable objects coexist, collision-free paths often do not exist. Planners that enforce collision-free trajectories therefore perform poorly by excluding feasible contact-aware trajectories. We introduce the deformation-guided acceptable-contact motion pl
Aerial docking of unmanned aerial vehicles (UAVs) is essential for advanced applications like persistent surveillance and in-flight recharging. Conventional underactuated multirotors, with only four control inputs for six degrees of freedom, lack independent control over their full pose. This fundam
Reinforcement Learning (RL) with Group Relative Policy Optimization (GRPO) shows great promise for enhancing LLM reasoning, but remains challenged by sparse and unstable rewards in long-horizon tasks. Existing approaches to reward shaping struggle to balance semantic expressiveness, reliability, and…
This letter focuses on the accuracy of LiDAR-inertial odometry (LIO). We propose a novel high-precision tightly-coupled LIO method, SI-LIO, based on the invariant extended Kalman filter with a single-iteration estimate update. This method utilizes the Lie exponential map between the matrix Lie group
Reinforcement learning (RL) for robotics poses significant consideration of safety during training. However, a major challenge of safe reinforcement learning (SRL) methods is the curse of dimensionality. Although existing SRL methods utilizing dimensionality reduction (DR) approaches could provide s
Addressing safe and efficient navigation in dynamic, realistic, and complex environments stands as a pivotal inquiry within the realm of robotics. Recently, numerous learning-based methods are introduced into the field of navigation, yielding notable outcomes. In this letter, we propose a hierarchic…
Integrating Large Language Models (LLMs) into robotics significantly enhances autonomous task planning. However, ensuring that multi-step task plans (action sequence) generated by LLMs comply with pre-defined safety constraints during planning and execution remains a challenge, limiting their adapta…
The role of a motion planner is pivotal in quadrotor applications, yet existing methods often struggle to adapt to complex environments, limiting their ability to achieve fast, safe, and robust flight. In this letter, we introduce a performance-enhanced quadrotor motion planner designed for autonomo
For real-world navigation, it is important to endow robots with the capabilities to navigate safely and efficiently in a complex environment with both dynamic and static obstacles. However, achieving path-finding in non-convex complex environments without maps as well as enabling multiple robots to
Visual servoing of Unmanned Aerial Vehicles (UAVs) has achieved satisfactory performance in fixed and planar motion targets. Due to highly coupled system dynamics and the sensitivity of the target image to aircraft attitude, the problem for chasing free-flying targets remains challenging. In this pa
A main challenge restricting the application of control barrier functions (CBFs) to complex scenarios is the absence of robustness against uncertainties induced by both measurements of the environment and robot dynamics. In this letter, we propose an uncertainty-aware, learning-based approach to con
Voronoi coverage control is a particular problem of importance in the area of multi-robot systems, which considers a network of multiple autonomous robots, tasked with optimally covering a large area. This is a common task for fleets of fixed-wing Unmanned Aerial Vehicles (UAVs), which are described…
This paper studies the problem of distributed computation of higher order Voronoi partition over a bounded region by a group of robots with both range-limited visibility sensors and communication devices. We model the sensing and communication capabilities by discs with limited radius. Motivated by…