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Found 6 papers, 2 papers with code
RoboMonkey: Scaling Test-Time Sampling and Verification for Vision-Language-Action Models
We first demonstrate that the relationship between action error and the number of generated samples follows an exponentiated power law across a range of VLAs, indicating the existence of inference-time scaling laws.
Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoning
FORTRESS outperforms on-the-fly prompting of slow reasoning models in safety classification accuracy on synthetic benchmarks and real-world ANYmal robot data, and further improves system safety and planning success in simulation and on quadrotor hardware for urban navigation.
Real-Time Anomaly Detection and Reactive Planning with Large Language Models
Foundation models, e. g., large language models (LLMs), trained on internet-scale data possess zero-shot generalization capabilities that make them a promising technology towards detecting and mitigating out-of-distribution failure modes of robotic systems.
STAP: Sequencing Task-Agnostic Policies
We further demonstrate how STAP can be used for task and motion planning by estimating the geometric feasibility of skill sequences provided by a task planner.
TASKOGRAPHY: Evaluating robot task planning over large 3D scene graphs
3D scene graphs (3DSGs) are an emerging description; unifying symbolic, topological, and metric scene representations.
S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds
With the increasing reliance of self-driving and similar robotic systems on robust 3D vision, the processing of LiDAR scans with deep convolutional neural networks has become a trend in academia and industry alike.
Ranked #3 on
3D Semantic Scene Completion
on SemanticKITTI
