8 accepted papers
Multi-robot motion planning and crowd simulations are crucial in social navigation, enabling agents to avoid collisions with one another in dynamic environments. While existing methods typically use simple circular models for robot and pedestrian boundaries, superquadric models offer greater flexibi…
Safe and efficient obstacle avoidance in multi-robot navigation is a challenging problem, with deadlock being a key issue due to strict collision-free constraints. This paper proposes a novel Lateral Reciprocal Collision Avoidance (LRCA) strategy based on velocity obstacle theory to mitigate deadloc…
End-to-end autonomous driving offers a stream-lined alternative to the traditional modular pipeline, integrating perception, prediction, and planning within a single framework. While Deep Reinforcement Learning (DRL) has recently gained traction in this domain, existing approaches often overlook the
Social navigation is a key consideration for integrating robots into human environments. Concurrently, it imposes heightened requisites: tasks must not only be executed succesfully without collisions, but also adhere to principles encompassing comprehensibility, courtesy, social compliance, comprehe…
Socially aware Object Goal Navigation (ObjectNav) requires robots to navigate to objects with specific semantic categories while understanding complex human social awareness and semantic co-occurrence relations among objects. Existing solutions usually achieve scene representation by mapping these c
We present CO-Net, a cohesive framework that optimizes multiple point cloud tasks collectively across heterogeneous dataset domains. CO-Net maintains the characteristics of high storage efficiency since models with the preponderance of shared parameters can be assembled into a single model. Specific…
In this work, we seek to predict camera poses across scenes with a multi-task learning manner, where we view the localization of each scene as a new task. We propose OFVL-MS, a unified framework that dispenses with the traditional practice of training a model for each individual scene and relieves…
Autonomous exploration in unknown environments is a fundamental task of Unmanned Aerial Vehicles (UAVs). To choose exploration goals wisely, we propose an information-driven exploration strategy by applying the fast marching method to exploration of UAVs. A frontier point detection algorithm is desi