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Found 7 papers, 4 papers with code
What Matters in Language Conditioned Robotic Imitation Learning over Unstructured Data
We have open-sourced our implementation to facilitate future research in learning to perform many complex manipulation skills in a row specified with natural language.
Affordance Learning from Play for Sample-Efficient Policy Learning
Robots operating in human-centered environments should have the ability to understand how objects function: what can be done with each object, where this interaction may occur, and how the object is used to achieve a goal.
CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks
We show that a baseline model based on multi-context imitation learning performs poorly on CALVIN, suggesting that there is significant room for developing innovative agents that learn to relate human language to their world models with this benchmark.
Pre-training of Deep RL Agents for Improved Learning under Domain Randomization
Visual domain randomization in simulated environments is a widely used method to transfer policies trained in simulation to real robots.
Hindsight for Foresight: Unsupervised Structured Dynamics Models from Physical Interaction
A key challenge for an agent learning to interact with the world is to reason about physical properties of objects and to foresee their dynamics under the effect of applied forces.
FlowControl: Optical Flow Based Visual Servoing
One-shot imitation is the vision of robot programming from a single demonstration, rather than by tedious construction of computer code.
Adaptive Curriculum Generation from Demonstrations for Sim-to-Real Visuomotor Control
We propose Adaptive Curriculum Generation from Demonstrations (ACGD) for reinforcement learning in the presence of sparse rewards.
