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Found 8 papers, 2 papers with code
Leveraging Contact Forces for Learning to Grasp
While it is possible to learn grasping policies without contact sensing, our results suggest that contact feedback allows for a significant improvement of grasping robustness under object pose uncertainty and for objects with a complex shape.
Shaping Belief States with Generative Environment Models for RL
We propose a way to efficiently train expressive generative models in complex environments.
Causally Correct Partial Models for Reinforcement Learning
In reinforcement learning, we can learn a model of future observations and rewards, and use it to plan the agent's next actions.
Probing Emergent Semantics in Predictive Agents via Question Answering
Recent work has shown how predictive modeling can endow agents with rich knowledge of their surroundings, improving their ability to act in complex environments.
Grounded Language Learning Fast and Slow
Recent work has shown that large text-based neural language models, trained with conventional supervised learning objectives, acquire a surprising propensity for few- and one-shot learning.
Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning
A common vision from science fiction is that robots will one day inhabit our physical spaces, sense the world as we do, assist our physical labours, and communicate with us through natural language.
Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding
Power-law scaling indicates that large-scale training with uniform sampling is prohibitively slow.
Scaling Instructable Agents Across Many Simulated Worlds
Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI.
