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Found 8 papers, 5 papers with code
Crossway Diffusion: Improving Diffusion-based Visuomotor Policy via Self-supervised Learning
Sequence modeling approaches have shown promising results in robot imitation learning.
Active Vision Reinforcement Learning under Limited Visual Observability
This learnable reward is assigned by sensorimotor reward module, incentivizes the sensory policy to select observations that are optimal to infer its own motor action, inspired by the sensorimotor stage of humans.
Neural Neural Textures Make Sim2Real Consistent
Unpaired image translation algorithms can be used for sim2real tasks, but many fail to generate temporally consistent results.
Learning Viewpoint-Agnostic Visual Representations by Recovering Tokens in 3D Space
To this end, we propose a 3D Token Representation Layer (3DTRL) that estimates the 3D positional information of the visual tokens and leverages it for learning viewpoint-agnostic representations.
Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?
We investigate whether self-supervised learning (SSL) can improve online reinforcement learning (RL) from pixels.
Enhancing Continual Learning with Global Prototypes: Counteracting Negative Representation Drift
Continual learning (CL) aims to learn a sequence of tasks over time, with data distributions shifting from one task to another.
StARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement Learning
Reinforcement Learning (RL) can be considered as a sequence modeling task: given a sequence of past state-action-reward experiences, an agent predicts a sequence of next actions.
Self-Supervised Disentangled Representation Learning for Third-Person Imitation Learning
Third-person imitation learning (TPIL) is the concept of learning action policies by observing other agents in a third-person view (TPV), similar to what humans do.
