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Found 11 papers, 2 papers with code
Aligning Images and Text with Semantic Role Labels for Fine-Grained Cross-Modal Understanding
As vision processing and natural language processing continue to advance, there is increasing interest in multimodal applications, such as image retrieval, caption generation, and human-robot interaction.
A Population-Level Analysis of Neural Dynamics in Robust Legged Robots
Recurrent neural network-based reinforcement learning systems are capable of complex motor control tasks such as locomotion and manipulation, however, much of their underlying mechanisms still remain difficult to interpret.
BO-ICP: Initialization of Iterative Closest Point Based on Bayesian Optimization
Typical algorithms for point cloud registration such as Iterative Closest Point (ICP) require a favorable initial transform estimate between two point clouds in order to perform a successful registration.
Time Dependence in Kalman Filter Tuning
First, in theory, many of these measures do not guarantee a unique solution due to observability issues.
Unsupervised Metric Relocalization Using Transform Consistency Loss
Training networks to perform metric relocalization traditionally requires accurate image correspondences.
Cooperative Control of Mobile Robots with Stackelberg Learning
Multi-robot cooperation requires agents to make decisions that are consistent with the shared goal without disregarding action-specific preferences that might arise from asymmetry in capabilities and individual objectives.
Leveraging Non-Specialists for Accurate and Time Efficient AMR Annotation
Abstract Meaning Representations (AMRs), a syntax-free representation of phrase semantics are useful for capturing the meaning of a phrase and reflecting the relationship between concepts that are referred to.
Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation
We consider solving a cooperative multi-robot object manipulation task using reinforcement learning (RL).
Weak in the NEES?: Auto-tuning Kalman Filters with Bayesian Optimization
Kalman filters are routinely used for many data fusion applications including navigation, tracking, and simultaneous localization and mapping problems.
Bayesian Optimization
Simultaneous Localization and Mapping
+1
Hidden Markov Random Field Iterative Closest Point
When registering point clouds resolved from an underlying 2-D pixel structure, such as those resulting from structured light and flash LiDAR sensors, or stereo reconstruction, it is expected that some points in one cloud do not have corresponding points in the other cloud, and that these would occur together, such as along an edge of the depth map.
Light Source Estimation with Analytical Path-tracing
We present a novel algorithm for light source estimation in scenes reconstructed with a RGB-D camera based on an analytically-derived formulation of path-tracing.
