Search Results for author:
Found 28 papers, 10 papers with code
Synapse: Learning Preferential Concepts from Visual Demonstrations
This paper addresses the problem of preference learning, which aims to learn user-specific preferences (e. g., "good parking spot", "convenient drop-off location") from visual input.
On a Foundation Model for Operating Systems
This paper lays down the research agenda for a domain-specific foundation model for operating systems (OSes).
Looking Inside Out: Anticipating Driver Intent From Videos
In this work, we propose a novel method of utilizing in-cabin and external camera data to improve state-of-the-art (SOTA) performance in predicting future driver actions.
STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience
Terrain awareness, i. e., the ability to identify and distinguish different types of terrain, is a critical ability that robots must have to succeed at autonomous off-road navigation.
ObVi-SLAM: Long-Term Object-Visual SLAM
Robots responsible for tasks over long time scales must be able to localize consistently and scalably amid geometric, viewpoint, and appearance changes.
Towards Robust Robot 3D Perception in Urban Environments: The UT Campus Object Dataset
Using our dataset and annotations, we release benchmarks for 3D object detection and 3D semantic segmentation using established metrics.
Wait, That Feels Familiar: Learning to Extrapolate Human Preferences for Preference Aligned Path Planning
In this work, we posit that operator preferences for visually novel terrains, which the robot should adhere to, can often be extrapolated from established terrain references within the inertial, proprioceptive, and tactile domain.
Learning Reward Machines through Preference Queries over Sequences
Reward machines have shown great promise at capturing non-Markovian reward functions for learning tasks that involve complex action sequencing.
Principles and Guidelines for Evaluating Social Robot Navigation Algorithms
A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation.
Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level Optimization
This paper presents a fully decentralized approach for realtime non-cooperative multi-robot navigation in social mini-games, such as navigating through a narrow doorway or negotiating right of way at a corridor intersection.
LLM+P: Empowering Large Language Models with Optimal Planning Proficiency
LLM+P takes in a natural language description of a planning problem, then returns a correct (or optimal) plan for solving that problem in natural language.
SOCIALGYM 2.0: Simulator for Multi-Agent Social Robot Navigation in Shared Human Spaces
We present SocialGym 2, a multi-agent navigation simulator for social robot research.
System Configuration and Navigation of a Guide Dog Robot: Toward Animal Guide Dog-Level Guiding Work
A robot guide dog has compelling advantages over animal guide dogs for its cost-effectiveness, potential for mass production, and low maintenance burden.
SOCIALMAPF: Optimal and Efficient Multi-Agent Path Finding with Strategic Agents for Social Navigation
We perform an extensive array of experiments that show that optimal search-based MAPF techniques lead to collisions and increased time-to-goal in SocialMAPF compared to our proposed method using mechanism design.
High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization
However, the types of control problems these approaches can be applied to are limited only to that of following pre-computed kinodynamically feasible trajectories.
Dense Crowd Flow-Informed Path Planning
In the case of pedestrian-unaware mobile robots this desire for safety leads to the freezing robot problem, where a robot confronted with a large dynamic group of obstacles (such as a crowd of pedestrians) would determine all forward navigation unsafe causing the robot to stop in place.
VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics
In this paper, we hypothesize that to enable accurate high-speed off-road navigation using a learned IKD model, in addition to inertial information from the past, one must also anticipate the kinodynamic interactions of the vehicle with the terrain in the future.
Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation
Social navigation is the capability of an autonomous agent, such as a robot, to navigate in a 'socially compliant' manner in the presence of other intelligent agents such as humans.
STEADY: Simultaneous State Estimation and Dynamics Learning from Indirect Observations
Accurate kinodynamic models play a crucial role in many robotics applications such as off-road navigation and high-speed driving.
Competence-Aware Path Planning via Introspective Perception
First, perception errors are learned in a model-free and location-agnostic setting via introspective perception prior to deployment in novel environments.
Visual Representation Learning for Preference-Aware Path Planning
We introduce Visual Representation Learning for Preference-Aware Path Planning (VRL-PAP), an alternative approach that overcomes all three limitations: VRL-PAP leverages unlabeled human demonstrations of navigation to autonomously generate triplets for learning visual representations of terrain that are viewpoint invariant and encode terrain types in a continuous representation space.
Iterative Program Synthesis for Adaptable Social Navigation
Robot social navigation is influenced by human preferences and environment-specific scenarios such as elevators and doors, thus necessitating end-user adaptability.
Program Synthesis
Robotics
Programming Languages
Robot Action Selection Learning via Layered Dimension Informed Program Synthesis
Action selection policies (ASPs), used to compose low-level robot skills into complex high-level tasks are commonly represented as neural networks (NNs) in the state of the art.
IV-SLAM: Introspective Vision for Simultaneous Localization and Mapping
Existing solutions to visual simultaneous localization and mapping (V-SLAM) assume that errors in feature extraction and matching are independent and identically distributed (i. i. d), but this assumption is known to not be true -- features extracted from low-contrast regions of images exhibit wider error distributions than features from sharp corners.
Learning to Optimize Autonomy in Competence-Aware Systems
Interest in semi-autonomous systems (SAS) is growing rapidly as a paradigm to deploy autonomous systems in domains that require occasional reliance on humans.
Localization under Topological Uncertainty for Lane Identification of Autonomous Vehicles
We present the Variable Structure Multiple Hidden Markov Model (VSM-HMM) as a framework for localizing in the presence of topological uncertainty, and demonstrate its effectiveness on an AV where lane membership is modeled as a topological localization process.
Interactive Robot Transition Repair With SMT
Complex robot behaviors are often structured as state machines, where states encapsulate actions and a transition function switches between states.
Robotics Programming Languages
Human-in-the-Loop SLAM
Building large-scale, globally consistent maps is a challenging problem, made more difficult in environments with limited access, sparse features, or when using data collected by novice users.
Human-Computer Interaction Robotics
