4 accepted papers
Articulated objects are ubiquitous in daily environments, and effective manipulation of these objects is essential for advancing open-world robotics. Existing approaches, which rely heavily on large-scale data collection or simulation, often face limitations in real-world applications, including iss
Dual-arm robotic systems hold great potential for complex bimanual tasks that require intricate and coordinated manipulation, such as holding and transporting a tray with a cup of coffee while navigating through cluttered environments. However, these tasks pose significant challenges due to the inhe
The data-driven paradigm has shown great potential in solving many decision-making tasks. In the robot navigation realm, it also sparked a new trend. People believe powerful data-driven methods can learn efficient and general navigation policies from a vast offline dataset. However, robot navigation
Reinforcement learning has shown great potential in improving the performance of robot navigation. In response to the increasing deployments of mobile robots within various scenarios, a data-driven paradigm of navigation approach with safety verification is preferred where one can train RL algorithm…