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Found 9 papers, 5 papers with code
An Unbiased Look at Datasets for Visuo-Motor Pre-Training
Visual representation learning hold great promise for robotics, but is severely hampered by the scarcity and homogeneity of robotics datasets.
MyoDex: A Generalizable Prior for Dexterous Manipulation
While prior work has synthesized single musculoskeletal control behaviors, MyoDex is the first generalizable manipulation prior that catalyzes the learning of dexterous physiological control across a large variety of contact-rich behaviors.
Learning Dexterous Manipulation from Exemplar Object Trajectories and Pre-Grasps
This paper seeks to escape these constraints, by developing a Pre-Grasp informed Dexterous Manipulation (PGDM) framework that generates diverse dexterous manipulation behaviors, without any task-specific reasoning or hyper-parameter tuning.
Model-Based Visual Planning with Self-Supervised Functional Distances
In our experiments, we find that our method can successfully learn models that perform a variety of tasks at test-time, moving objects amid distractors with a simulated robotic arm and even learning to open and close a drawer using a real-world robot.
Transformers for One-Shot Visual Imitation
Humans are able to seamlessly visually imitate others, by inferring their intentions and using past experience to achieve the same end goal.
RoboNet: Large-Scale Multi-Robot Learning
This leads to a frequent tension in robotic learning: how can we learn generalizable robotic controllers without having to collect impractically large amounts of data for each separate experiment?
Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control
Deep reinforcement learning (RL) algorithms can learn complex robotic skills from raw sensory inputs, but have yet to achieve the kind of broad generalization and applicability demonstrated by deep learning methods in supervised domains.
Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning
We demonstrate that this idea can be combined with a video-prediction based controller to enable complex behaviors to be learned from scratch using only raw visual inputs, including grasping, repositioning objects, and non-prehensile manipulation.
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning
Humans and animals are capable of learning a new behavior by observing others perform the skill just once.
