4 citations · 4 across the 1 of their papers we have counts for
5 papers · 1 filter
Efficient Hierarchical Any-Angle Path Planning on Multi-Resolution 3D Grids
Victor Reijgwart, Cesar Cadena, Roland Siegwart +1
Hierarchical, multi-resolution volumetric mapping approaches are widely used to represent large and complex environments as they can efficiently capture their occupancy and connect…
CueLearner: Bootstrapping and local policy adaptation from relative feedback
Giulio Schiavi, Andrei Cramariuc, Lionel Ott +1
Human guidance has emerged as a powerful tool for enhancing reinforcement learning (RL). However, conventional forms of guidance such as demonstrations or binary scalar feedback ca…
Learning Affordances from Interactive Exploration using an Object-level Map
Paula Wulkop, Halil Umut Ãzdemir, Antonia Hüfner +3
Many robotic tasks in real-world environments require physical interactions with an object such as pick up or push. For successful interactions, the robot needs to know the object'…
Task Adaptation in Industrial Human-Robot Interaction: Leveraging Riemannian Motion Policies
Mike Allenspach, Michael Pantic, Rik Girod +2
In real-world industrial environments, modern robots often rely on human operators for crucial decision-making and mission synthesis from individual tasks. Effective and safe colla…
Waverider: Leveraging Hierarchical, Multi-Resolution Maps for Efficient and Reactive Obstacle Avoidance
Victor Reijgwart, Michael Pantic, Roland Siegwart +1
Fast and reliable obstacle avoidance is an important task for mobile robots. In this work, we propose an efficient reactive system that provides high-quality obstacle avoidance whi…