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Found 8 papers, 3 papers with code
Emergent Active Perception and Dexterity of Simulated Humanoids from Visual Reinforcement Learning
Human behavior is fundamentally shaped by visual perception -- our ability to interact with the world depends on actively gathering relevant information and adapting our movements accordingly.
Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids
This work investigates the key challenges in applying reinforcement learning to solve a collection of contact-rich manipulation tasks on a humanoid embodiment.
Learning Visuotactile Skills with Two Multifingered Hands
Two significant challenges exist: the lack of an affordable and accessible teleoperation system suitable for a dual-arm setup with multifingered hands, and the scarcity of multifingered hand hardware equipped with touch sensing.
Twisting Lids Off with Two Hands
Manipulating objects with two multi-fingered hands has been a long-standing challenge in robotics, due to the contact-rich nature of many manipulation tasks and the complexity inherent in coordinating a high-dimensional bimanual system.
MIMEx: Intrinsic Rewards from Masked Input Modeling
We show how this perspective naturally leads to a unified view on existing intrinsic reward approaches: they are special cases of conditional prediction, where the estimation of novelty can be seen as pseudo-likelihood estimation with different mask distributions.
Learning to Ground Multi-Agent Communication with Autoencoders
Communication requires having a common language, a lingua franca, between agents.
Visual Grounding of Learned Physical Models
The abilities to perform physical reasoning and to adapt to new environments, while intrinsic to humans, remain challenging to state-of-the-art computational models.
Model Based Planning with Energy Based Models
We provide an online algorithm to train EBMs while interacting with the environment, and show that EBMs allow for significantly better online learning than corresponding feed-forward networks.
