Roboagent: Generalization and efficiency in robot manipulation via semantic augmentations and action chunking
The grand aim of having a single robot that can manipulate arbitrary objects in diverse
settings is at odds with the paucity of robotics datasets. Acquiring and growing such datasets …
settings is at odds with the paucity of robotics datasets. Acquiring and growing such datasets …
Learning multi-step manipulation tasks from a single human demonstration
D Guo - arXiv preprint arXiv:2312.15346, 2023 - arxiv.org
Learning from human demonstrations has exhibited remarkable achievements in robot
manipulation. However, the challenge remains to develop a robot system that matches …
manipulation. However, the challenge remains to develop a robot system that matches …
ARNOLD: A benchmark for language-grounded task learning with continuous states in realistic 3D scenes
Understanding the continuous states of objects is essential for task learning and planning in
the real world. However, most existing task learning benchmarks assume discrete (eg …
the real world. However, most existing task learning benchmarks assume discrete (eg …
Contrastive language, action, and state pre-training for robot learning
In this paper, we introduce a method for unifying language, action, and state information in a
shared embedding space to facilitate a range of downstream tasks in robot learning. Our …
shared embedding space to facilitate a range of downstream tasks in robot learning. Our …
Lancon-learn: Learning with language to enable generalization in multi-task manipulation
Robots must be capable of learning from previously solved tasks and generalizing that
knowledge to quickly perform new tasks to realize the vision of ubiquitous and useful robot …
knowledge to quickly perform new tasks to realize the vision of ubiquitous and useful robot …
Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks
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 …
human language to their perceptions and actions to be useful in a range of daily tasks …
Language-driven representation learning for robotics
Recent work in visual representation learning for robotics demonstrates the viability of
learning from large video datasets of humans performing everyday tasks. Leveraging …
learning from large video datasets of humans performing everyday tasks. Leveraging …
Deep imitation learning for bimanual robotic manipulation
F Xie, A Chowdhury… - Advances in neural …, 2020 - proceedings.neurips.cc
We present a deep imitation learning framework for robotic bimanual manipulation in a
continuous state-action space. A core challenge is to generalize the manipulation skills to …
continuous state-action space. A core challenge is to generalize the manipulation skills to …
Augmenting reinforcement learning with behavior primitives for diverse manipulation tasks
Realistic manipulation tasks require a robot to interact with an environment with a prolonged
sequence of motor actions. While deep reinforcement learning methods have recently …
sequence of motor actions. While deep reinforcement learning methods have recently …
Vid2robot: End-to-end video-conditioned policy learning with cross-attention transformers
While large-scale robotic systems typically rely on textual instructions for tasks, this work
explores a different approach: can robots infer the task directly from observing humans? This …
explores a different approach: can robots infer the task directly from observing humans? This …