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Found 4 papers, 0 papers with code
SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning
To ensure the scalability of our approach, we: (1) exploit the hierarchical nature of 3DSGs to allow LLMs to conduct a 'semantic search' for task-relevant subgraphs from a smaller, collapsed representation of the full graph; (2) reduce the planning horizon for the LLM by integrating a classical path planner and (3) introduce an 'iterative replanning' pipeline that refines the initial plan using feedback from a scene graph simulator, correcting infeasible actions and avoiding planning failures.
ParticleNeRF: A Particle-Based Encoding for Online Neural Radiance Fields
ParticleNeRF is the first online dynamic NeRF and achieves fast adaptability with better visual fidelity than brute-force online InstantNGP and other baseline approaches on dynamic scenes with online constraints.
Learning Fabric Manipulation in the Real World with Human Videos
A promising alternative is to learn fabric manipulation directly from watching humans perform the task.
Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance Fields
We show that ensembling effectively quantifies model uncertainty in Neural Radiance Fields (NeRFs) if a density-aware epistemic uncertainty term is considered.
