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Deep reinforcement learning for robotics: A survey of real-world successes
Reinforcement learning (RL), particularly its combination with deep neural networks,
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …
Learning multi-object dynamics with compositional neural radiance fields
We present a method to learn compositional multi-object dynamics models from image
observations based on implicit object encoders, Neural Radiance Fields (NeRFs), and …
observations based on implicit object encoders, Neural Radiance Fields (NeRFs), and …
Long-horizon multi-robot rearrangement planning for construction assembly
Robotic construction assembly planning aims to find feasible assembly sequences as well
as the corresponding robot-paths and can be seen as a special case of task and motion …
as the corresponding robot-paths and can be seen as a special case of task and motion …
Robotic architectural assembly with tactile skills: Simulation and optimization
Construction is an industry that could benefit significantly from automation, yet still relies
heavily on manual human labor. Thus, we investigate how a robotic arm can be used to …
heavily on manual human labor. Thus, we investigate how a robotic arm can be used to …
[HTML][HTML] Learning to reason over scene graphs: a case study of finetuning GPT-2 into a robot language model for grounded task planning
Long-horizon task planning is essential for the development of intelligent assistive and
service robots. In this work, we investigate the applicability of a smaller class of large …
service robots. In this work, we investigate the applicability of a smaller class of large …
Dynamic-resolution model learning for object pile manipulation
Dynamics models learned from visual observations have shown to be effective in various
robotic manipulation tasks. One of the key questions for learning such dynamics models is …
robotic manipulation tasks. One of the key questions for learning such dynamics models is …
Blocks assemble! learning to assemble with large-scale structured reinforcement learning
Assembly of multi-part physical structures is both a valuable end product for autonomous
robotics, as well as a valuable diagnostic task for open-ended training of embodied …
robotics, as well as a valuable diagnostic task for open-ended training of embodied …
Graph learning in robotics: a survey
Deep neural networks for graphs have emerged as a powerful tool for learning on complex
non-euclidean data, which is becoming increasingly common for a variety of different …
non-euclidean data, which is becoming increasingly common for a variety of different …
Learning physically realizable skills for online packing of general 3D shapes
We study the problem of learning online packing skills for irregular 3D shapes, which is
arguably the most challenging setting of bin packing problems. The goal is to consecutively …
arguably the most challenging setting of bin packing problems. The goal is to consecutively …
Efficient and feasible robotic assembly sequence planning via graph representation learning
Automatic Robotic Assembly Sequence Planning (RASP) can significantly improve
productivity and resilience in modern manufacturing along with the growing need for greater …
productivity and resilience in modern manufacturing along with the growing need for greater …