Search Results for author:
Found 20 papers, 4 papers with code
Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination
In our new Dynamic Learning from Learned Hallucination (Dyna-LfLH), we design and learn a novel latent distribution and sample dynamic obstacles from it, so the generated training data can be used to learn a motion planner to navigate in dynamic environments.
VANP: Learning Where to See for Navigation with Self-Supervised Vision-Action Pre-Training
However, most robotic visual navigation methods rely on deep learning models pre-trained on vision tasks, which prioritize salient objects -- not necessarily relevant to navigation and potentially misleading.
Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning
To address the inefficiency of tracking distant navigation goals, we introduce a hierarchical locomotion controller that combines a classical planner tasked with planning waypoints to reach a faraway global goal location, and an RL-based policy trained to follow these waypoints by generating low-level motion commands.
Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning
Two desiderata of reinforcement learning (RL) algorithms are the ability to learn from relatively little experience and the ability to learn policies that generalize to a range of problem specifications.
A Study on Learning Social Robot Navigation with Multimodal Perception
Autonomous mobile robots need to perceive the environments with their onboard sensors (e. g., LiDARs and RGB cameras) and then make appropriate navigation decisions.
How susceptible are LLMs to Logical Fallacies?
Then, it evaluates the debater's performance in logical reasoning by contrasting the scenario where the persuader employs logical fallacies against one where logical reasoning is used.
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.
Benchmarking Reinforcement Learning Techniques for Autonomous Navigation
Deep reinforcement learning (RL) has brought many successes for autonomous robot navigation.
Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation
Despite decades of research, existing navigation systems still face real-world challenges when deployed in the wild, e. g., in cluttered home environments or in human-occupied public spaces.
Causal Dynamics Learning for Task-Independent State Abstraction
Learning dynamics models accurately is an important goal for Model-Based Reinforcement Learning (MBRL), but most MBRL methods learn a dense dynamics model which is vulnerable to spurious correlations and therefore generalizes poorly to unseen states.
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.
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.
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.
Conflict Avoidance in Social Navigation -- a Survey
This survey aims to bridge this gap by introducing such a common language, using it to survey existing work, and highlighting open problems.
VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigation
Is imitation learning for vision based autonomous navigation even possible in such scenarios?
APPLD: Adaptive Planner Parameter Learning from Demonstration
Existing autonomous robot navigation systems allow robots to move from one point to another in a collision-free manner.
Tethered Aerial Visual Assistance
In this paper, an autonomous tethered Unmanned Aerial Vehicle (UAV) is developed into a visual assistant in a marsupial co-robots team, collaborating with a tele-operated Unmanned Ground Vehicle (UGV) for robot operations in unstructured or confined environments.
Autonomous Visual Assistance for Robot Operations Using a Tethered UAV
This paper develops an autonomous tethered aerial visual assistant for robot operations in unstructured or confined environments.
Robotics
Explicit-risk-aware Path Planning with Reward Maximization
Without manual assignment of the negative impact to the planner caused by risk, this planner takes in a pre-established viewpoint quality map and plans target location and path leading to it simultaneously, in order to maximize overall reward along the entire path while minimizing risk.
