Zobrazeno 1 - 10
of 19
pro vyhledávání: '"de Heuvel, Jorge"'
Autor:
Kreis, Benedikt, Dengler, Nils, de Heuvel, Jorge, Menon, Rohit, Perur, Hamsa Datta, Bennewitz, Maren
Close and precise placement of irregularly shaped objects requires a skilled robotic system. The manipulation of objects that have sensitive top surfaces and a fixed set of neighbors is particularly challenging. To avoid damaging the surface, the rob
Externí odkaz:
http://arxiv.org/abs/2404.10632
Mobile robots are increasingly being used in noisy environments for social purposes, e.g. to provide support in healthcare or public spaces. Since these robots also operate beyond human sight, the question arises as to how different robot types, ambi
Externí odkaz:
http://arxiv.org/abs/2404.06807
To align mobile robot navigation policies with user preferences through reinforcement learning from human feedback (RLHF), reliable and behavior-diverse user queries are required. However, deterministic policies fail to generate a variety of navigati
Externí odkaz:
http://arxiv.org/abs/2404.04852
Preference-aligned robot navigation in human environments is typically achieved through learning-based approaches, utilizing demonstrations and user feedback for personalization. However, personal preferences are subject to change and might even be c
Externí odkaz:
http://arxiv.org/abs/2404.04857
Autor:
Schlachhoff, Erik, Dengler, Nils, Van Holland, Leif, Stotko, Patrick, de Heuvel, Jorge, Klein, Reinhard, Bennewitz, Maren
In 1997, the very first tour guide robot RHINO was deployed in a museum in Germany. With the ability to navigate autonomously through the environment, the robot gave tours to over 2,000 visitors. Today, RHINO itself has become an exhibit and is no lo
Externí odkaz:
http://arxiv.org/abs/2403.15151
Foresighted robot navigation in dynamic indoor environments with cost-efficient hardware necessitates the use of a lightweight yet dependable controller. So inferring the scene dynamics from sensor readings without explicit object tracking is a pivot
Externí odkaz:
http://arxiv.org/abs/2310.19670
Collision-free, goal-directed navigation in environments containing unknown static and dynamic obstacles is still a great challenge, especially when manual tuning of navigation policies or costly motion prediction needs to be avoided. In this paper,
Externí odkaz:
http://arxiv.org/abs/2303.01443
When arranging objects with robotic arms, the quality of the end result strongly depends on the achievable placement accuracy. However, even the most advanced robotic systems are prone to positioning errors that can occur at different steps of the ma
Externí odkaz:
http://arxiv.org/abs/2302.07795
Autor:
de Heuvel, Jorge, Corral, Nathan, Kreis, Benedikt, Conradi, Jacobus, Driemel, Anne, Bennewitz, Maren
For the best human-robot interaction experience, the robot's navigation policy should take into account personal preferences of the user. In this paper, we present a learning framework complemented by a perception pipeline to train a depth vision-bas
Externí odkaz:
http://arxiv.org/abs/2210.01683
Reinforcement learning (RL) has recently proven great success in various domains. Yet, the design of the reward function requires detailed domain expertise and tedious fine-tuning to ensure that agents are able to learn the desired behaviour. Using a
Externí odkaz:
http://arxiv.org/abs/2210.01525