Search Results for author:
Found 43 papers, 8 papers with code
Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning
The design of these low-level heuristics has a major impact on the quality of the result.
Constrained Robotic Navigation on Preferred Terrains Using LLMs and Speech Instruction: Exploiting the Power of Adverbs
This paper explores leveraging large language models for map-free off-road navigation using generative AI, reducing the need for traditional data collection and annotation.
A Neural-Evolutionary Algorithm for Autonomous Transit Network Design
Planning a public transit network is a challenging optimization problem, but essential in order to realize the benefits of autonomous buses.
A comparison of RL-based and PID controllers for 6-DOF swimming robots: hybrid underwater object tracking
We use established methods for vision-based tracking and introduce a centralized DQN controller.
PhotoBot: Reference-Guided Interactive Photography via Natural Language
We leverage a visual language model (VLM) and an object detector to characterize the reference images via textual descriptions and then use a large language model (LLM) to retrieve relevant reference images based on a user's language query through text-based reasoning.
Hallucination Detection and Hallucination Mitigation: An Investigation
Large language models (LLMs), including ChatGPT, Bard, and Llama, have achieved remarkable successes over the last two years in a range of different applications.
Device-Free Human State Estimation using UWB Multi-Static Radios
We make use of the channel impulse response (CIR) measurements from the UWB sensors to estimate the human state - comprised of location and activity - in a given area.
Anomaly Detection for Scalable Task Grouping in Reinforcement Learning-based RAN Optimization
This coincides with a rapid increase in the number of cell sites worldwide, driven largely by dramatic growth in cellular network traffic.
Working Backwards: Learning to Place by Picking
By doing so, the policy can generalize to object placement scenarios outside of the training environment without privileged information (e. g., placing a plate picked up from a table).
Learning active tactile perception through belief-space control
These robots will need to sense these properties through interaction prior to performing downstream tasks with the objects.
Generalizable Imitation Learning Through Pre-Trained Representations
In this paper we leverage self-supervised vision transformer models and their emergent semantic abilities to improve the generalization abilities of imitation learning policies.
Multimodal and Force-Matched Imitation Learning with a See-Through Visuotactile Sensor
An STS sensor can be switched between visual and tactile modes by leveraging a semi-transparent surface and controllable lighting, allowing for both pre-contact visual sensing and during-contact tactile sensing with a single sensor.
Adaptive Dynamic Programming for Energy-Efficient Base Station Cell Switching
In this work, we propose an approximate dynamic programming (ADP)-based method coupled with online optimization to switch on/off the cells of base stations to reduce network power consumption while maintaining adequate Quality of Service (QoS) metrics.
Imitation Learning from Observation through Optimal Transport
Imitation Learning from Observation (ILfO) is a setting in which a learner tries to imitate the behavior of an expert, using only observational data and without the direct guidance of demonstrated actions.
CARTIER: Cartographic lAnguage Reasoning Targeted at Instruction Execution for Robots
This work explores the capacity of large language models (LLMs) to address problems at the intersection of spatial planning and natural language interfaces for navigation.
Neural Bee Colony Optimization: A Case Study in Public Transit Network Design
In this work we explore the combination of metaheuristics and learned neural network solvers for combinatorial optimization.
Policy Reuse for Communication Load Balancing in Unseen Traffic Scenarios
When deploying to an unknown traffic scenario, we select a policy from the policy bank based on the similarity between the previous-day traffic of the current scenario and the traffic observed during training.
Communication Load Balancing via Efficient Inverse Reinforcement Learning
Engineering this reward function is challenging, because it involves the need for expert knowledge and there lacks a general consensus on the form of an optimal reward function.
Multi-agent Attention Actor-Critic Algorithm for Load Balancing in Cellular Networks
In cellular networks, User Equipment (UE) handoff from one Base Station (BS) to another, giving rise to the load balancing problem among the BSs.
Self-Supervised Transformer Architecture for Change Detection in Radio Access Networks
Such networks undergo frequent and often heterogeneous changes caused by network operators, who are seeking to tune their system parameters for optimal performance.
Hypernetworks for Zero-shot Transfer in Reinforcement Learning
In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objective and data from a set of near-optimal RL solutions for training tasks.
Bayesian Q-learning With Imperfect Expert Demonstrations
Guided exploration with expert demonstrations improves data efficiency for reinforcement learning, but current algorithms often overuse expert information.
IL-flOw: Imitation Learning from Observation using Normalizing Flows
We present an algorithm for Inverse Reinforcement Learning (IRL) from expert state observations only.
Trajectory-Constrained Deep Latent Visual Attention for Improved Local Planning in Presence of Heterogeneous Terrain
We present a reward-predictive, model-based deep learning method featuring trajectory-constrained visual attention for local planning in visual navigation tasks.
Average Outward Flux Skeletons for Environment Mapping and Topology Matching
In topology matching between two given maps and their AOF skeletons, we first find correspondences between points on the AOF skeletons of two different environments.
Multi-batch Reinforcement Learning via Sample Transfer and Imitation Learning
Unfortunately, most online reinforcement learning algorithms require a large number of interactions with the environment to learn a reliable control policy.
MOBA: Multi-teacher Model Based Reinforcement Learning
Results show that, compared with SOTA model-free methods, our method can improve the data efficiency and system performance by up to 75% and 10%, respectively.
Sample Efficient Social Navigation Using Inverse Reinforcement Learning
In this paper, we present an algorithm to efficiently learn socially-compliant navigation policies from observations of human trajectories.
Scalable Multirobot Planning for Informed Spatial Sampling
This paper presents a distributed scalable multi-robot planning algorithm for informed sampling of quasistatic spatial fields.
Learning Intuitive Physics with Multimodal Generative Models
Predicting the future interaction of objects when they come into contact with their environment is key for autonomous agents to take intelligent and anticipatory actions.
Learning to Drive Off Road on Smooth Terrain in Unstructured Environments Using an On-Board Camera and Sparse Aerial Images
We present a method for learning to drive on smooth terrain while simultaneously avoiding collisions in challenging off-road and unstructured outdoor environments using only visual inputs.
One-Shot Informed Robotic Visual Search in the Wild
In this paper we propose a method that enables informed visual navigation via a learned visual similarity operator that guides the robot's visual search towards parts of the scene that look like an exemplar image, which is given by the user as a high-level specification for data collection.
DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization
In this paper, we propose a real-time deep learning approach for determining the 6D relative pose of Autonomous Underwater Vehicles (AUV) from a single image.
Detecting GAN generated errors
This is because the task of evaluating the quality of a generated image differs from deciding if an image is real or fake.
Reinforcement Learning with Non-uniform State Representations for Adaptive Search
In this paper, we present a search algorithm that generates efficient trajectories that optimize the rate at which probability mass is covered by a searcher.
Learning Domain Randomization Distributions for Training Robust Locomotion Policies
We explore the use of gradient-based search methods to learn a domain randomization with the following properties: 1) The trained policy should be successful in environments sampled from the domain randomization distribution 2) The domain randomization distribution should be wide enough so that the experience similar to the target robot system is observed during training, while addressing the practicality of training finite capacity models.
Planning in Dynamic Environments with Conditional Autoregressive Models
We demonstrate the use of conditional autoregressive generative models (van den Oord et al., 2016a) over a discrete latent space (van den Oord et al., 2017b) for forward planning with MCTS.
Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning
Finally, we assess the performance of the algorithm for learning motor controllers for a six legged autonomous underwater vehicle.
Model-based Reinforcement Learning
reinforcement-learning
+1
Scale-Robust Localization Using General Object Landmarks
Visual localization under large changes in scale is an important capability in many robotic mapping applications, such as localizing at low altitudes in maps built at high altitudes, or performing loop closure over long distances.
Underwater Multi-Robot Convoying using Visual Tracking by Detection
We present a robust multi-robot convoying approach that relies on visual detection of the leading agent, thus enabling target following in unstructured 3-D environments.
Benchmark Environments for Multitask Learning in Continuous Domains
As demand drives systems to generalize to various domains and problems, the study of multitask, transfer and lifelong learning has become an increasingly important pursuit.
Modeling Curiosity in a Mobile Robot for Long-Term Autonomous Exploration and Monitoring
This paper presents a novel approach to modeling curiosity in a mobile robot, which is useful for monitoring and adaptive data collection tasks, especially in the context of long term autonomous missions where pre-programmed missions are likely to have limited utility.
Gibbs Sampling Strategies for Semantic Perception of Streaming Video Data
Topic modeling of streaming sensor data can be used for high level perception of the environment by a mobile robot.
