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Found 5 papers, 4 papers with code
Grasp, See and Place: Efficient Unknown Object Rearrangement with Policy Structure Prior
For the inner loop, we learn an active seeing policy for self-confident object matching to improve the perception of place.
Object-centric Inference for Language Conditioned Placement: A Foundation Model based Approach
We focus on the task of language-conditioned object placement, in which a robot should generate placements that satisfy all the spatial relational constraints in language instructions.
E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Context
However, the redundant parameters within the network structure can cause a large model size when scaling up for desirable performance.
Ranked #4 on
Video Reconstruction
on UVG
Learning A Simulation-based Visual Policy for Real-world Peg In Unseen Holes
This paper proposes a learning-based visual peg-in-hole that enables training with several shapes in simulation, and adapting to arbitrary unseen shapes in real world with minimal sim-to-real cost.
Efficient learning of goal-oriented push-grasping synergy in clutter
In this paper, a goal-conditioned hierarchical reinforcement learning formulation with high sample efficiency is proposed to learn a push-grasping policy for grasping a specific object in clutter.
Hierarchical Reinforcement Learning
Robotics
