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Found 5 papers, 1 papers with code
Self-Supervised Learning of Motion Concepts by Optimizing Counterfactuals
Estimating motion in videos is an essential computer vision problem with many downstream applications, including controllable video generation and robotics.
Understanding Physical Dynamics with Counterfactual World Modeling
The ability to understand physical dynamics is critical for agents to act in the world.
Unifying (Machine) Vision via Counterfactual World Modeling
Leading approaches in machine vision employ different architectures for different tasks, trained on costly task-specific labeled datasets.
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation
We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation.
Flexible and Efficient Long-Range Planning Through Curious Exploration
In contrast, deep reinforcement learning (DRL) methods use flexible neural-network-based function approximators to discover policies that generalize naturally to unseen circumstances.
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