Search Results for author:
Found 13 papers, 7 papers with code
Dial-insight: Fine-tuning Large Language Models with High-Quality Domain-Specific Data Preventing Capability Collapse
The efficacy of large language models (LLMs) is heavily dependent on the quality of the underlying data, particularly within specialized domains.
A System for Generalized 3D Multi-Object Search
GenMOS takes as input point cloud observations of the local region, object detection results, and localization of the robot's view pose, and outputs a 6D viewpoint to move to through online planning.
Hierarchical Reinforcement Learning of Locomotion Policies in Response to Approaching Objects: A Preliminary Study
Animals such as rabbits and birds can instantly generate locomotion behavior in reaction to a dynamic, approaching object, such as a person or a rock, despite having possibly never seen the object before and having limited perception of the object's properties.
Towards Optimal Correlational Object Search
In realistic applications of object search, robots will need to locate target objects in complex environments while coping with unreliable sensors, especially for small or hard-to-detect objects.
Dialogue Object Search
To this end, we introduce a new task, dialogue object search: A robot is tasked to search for a target object (e. g. fork) in a human environment (e. g., kitchen), while engaging in a "video call" with a remote human who has additional but inexact knowledge about the target's location.
Spatial Language Understanding for Object Search in Partially Observed City-scale Environments
We propose SLOOP (Spatial Language Object-Oriented POMDP), a new framework for partially observable decision making with a probabilistic observation model for spatial language.
Multi-Resolution POMDP Planning for Multi-Object Search in 3D
Robots operating in households must find objects on shelves, under tables, and in cupboards.
pomdp_py: A Framework to Build and Solve POMDP Problems
In this paper, we present pomdp_py, a general purpose Partially Observable Markov Decision Process (POMDP) library written in Python and Cython.
From Pixels to Buildings: End-to-end Probabilistic Deep Networks for Large-scale Semantic Mapping
We introduce TopoNets, end-to-end probabilistic deep networks for modeling semantic maps with structure reflecting the topology of large-scale environments.
Learning Graph-Structured Sum-Product Networks for Probabilistic Semantic Maps
We introduce Graph-Structured Sum-Product Networks (GraphSPNs), a probabilistic approach to structured prediction for problems where dependencies between latent variables are expressed in terms of arbitrary, dynamic graphs.
ROS Navigation Tuning Guide
This article intends to guide the reader through the process of fine tuning navigation parameters.
Robotics
Learning Large-Scale Topological Maps Using Sum-Product Networks
Our experiments evaluate the ability of our models to enable robots to infer semantic attributes and detect maps with novel semantic attribute arrangements.
