911 citations · 2.9k across the 99 of their papers we have counts for
12 papers · 1 filter
CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks
Oier Mees, Lukas Hermann, Erick Rosete-Beas +1
General-purpose robots coexisting with humans in their environment must learn to relate human language to their perceptions and actions to be useful in a range of daily tasks. More…
Robot Skill Adaptation via Soft Actor-Critic Gaussian Mixture Models
Iman Nematollahi, Erick Rosete-Beas, Adrian Röfer +3
A core challenge for an autonomous agent acting in the real world is to adapt its repertoire of skills to cope with its noisy perception and dynamics. To scale learning of skills t…
Adaptively Calibrated Critic Estimates for Deep Reinforcement Learning
Nicolai Dorka, Tim Welschehold, Joschka Boedecker +1
Accurate value estimates are important for off-policy reinforcement learning. Algorithms based on temporal difference learning typically are prone to an over- or underestimation bi…
Robust Monocular Localization in Sparse HD Maps Leveraging Multi-Task Uncertainty Estimation
Kürsat Petek, Kshitij Sirohi, Daniel Büscher +1
Robust localization in dense urban scenarios using a low-cost sensor setup and sparse HD maps is highly relevant for the current advances in autonomous driving, but remains a chall…
Correct Me if I am Wrong: Interactive Learning for Robotic Manipulation
Eugenio Chisari, Tim Welschehold, Joschka Boedecker +2
Learning to solve complex manipulation tasks from visual observations is a dominant challenge for real-world robot learning. Although deep reinforcement learning algorithms have re…
Courteous Behavior of Automated Vehicles at Unsignalized Intersections via Reinforcement Learning
Shengchao Yan, Tim Welschehold, Daniel Büscher +1
The transition from today's mostly human-driven traffic to a purely automated one will be a gradual evolution, with the effect that we will likely experience mixed traffic in the n…
Lane Graph Estimation for Scene Understanding in Urban Driving
Jannik Zürn, Johan Vertens, Wolfram Burgard
Lane-level scene annotations provide invaluable data in autonomous vehicles for trajectory planning in complex environments such as urban areas and cities. However, obtaining such…
Pre-training of Deep RL Agents for Improved Learning under Domain Randomization
Artemij Amiranashvili, Max Argus, Lukas Hermann +2
Visual domain randomization in simulated environments is a widely used method to transfer policies trained in simulation to real robots. However, domain randomization and augmentat…
Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion
Vitor Guizilini, Rares Ambrus, Wolfram Burgard +1
Estimating scene geometry from data obtained with cost-effective sensors is key for robots and self-driving cars. In this paper, we study the problem of predicting dense depth from…
Learning to Track with Object Permanence
Pavel Tokmakov, Jie Li, Wolfram Burgard +1
Tracking by detection, the dominant approach for online multi-object tracking, alternates between localization and association steps. As a result, it strongly depends on the qualit…
Composing Pick-and-Place Tasks By Grounding Language
Oier Mees, Wolfram Burgard
Controlling robots to perform tasks via natural language is one of the most challenging topics in human-robot interaction. In this work, we present a robot system that follows unco…
EfficientLPS: Efficient LiDAR Panoptic Segmentation
Kshitij Sirohi, Rohit Mohan, Daniel Büscher +2
Panoptic segmentation of point clouds is a crucial task that enables autonomous vehicles to comprehend their vicinity using their highly accurate and reliable LiDAR sensors. Existi…