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Found 9 papers, 5 papers with code
DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo
Dense 3D correspondence can enhance robotic manipulation by enabling the generalization of spatial, functional, and dynamic information from one object to an unseen counterpart.
Stem-OB: Generalizable Visual Imitation Learning with Stem-Like Convergent Observation through Diffusion Inversion
Visual imitation learning methods demonstrate strong performance, yet they lack generalization when faced with visual input perturbations, including variations in lighting and textures, impeding their real-world application.
Make-An-Agent: A Generalizable Policy Network Generator with Behavior-Prompted Diffusion
Can we generate a control policy for an agent using just one demonstration of desired behaviors as a prompt, as effortlessly as creating an image from a textual description?
Rethinking Transformers in Solving POMDPs
Sequential decision-making algorithms such as reinforcement learning (RL) in real-world scenarios inevitably face environments with partial observability.
Robo-ABC: Affordance Generalization Beyond Categories via Semantic Correspondence for Robot Manipulation
The next step is to map the contact points of the retrieved objects to the new object.
Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization
Owning to the alignment of objectives in two phases, the RL agent can transfer between offline and online learning seamlessly.
RL-ViGen: A Reinforcement Learning Benchmark for Visual Generalization
Visual Reinforcement Learning (Visual RL), coupled with high-dimensional observations, has consistently confronted the long-standing challenge of out-of-distribution generalization.
Out-of-Distribution Generalization
reinforcement-learning
+1
Decision Transformer under Random Frame Dropping
Typical RL methods usually require considerable online interaction data that are costly and unsafe to collect in the real world.
Extraneousness-Aware Imitation Learning
To tackle this problem, we introduce Extraneousness-Aware Imitation Learning (EIL), a self-supervised approach that learns visuomotor policies from third-person demonstrations with extraneous subsequences.
