24 accepted papers
Heterogeneous robot teams executing Linear Temporal Logic (LTL) missions are usually modeled with fixed robot capabilities. In practice, capabilities may change during execution through tool acquisition, sensor activation, or module reconfiguration, making previously infeasible tasks executable and
Designing a whole-body controller for loco-manipulation in unstructured real-world environments remains a formidable challenge. Previous approaches have primarily focused on extending the workspace of robotic arms while maintaining quadrupedal landing postures. However, these methods fail to fully e
Efficiently searching for multiple objects in a partially known environment, where only the names and locations of landmarks are available, presents significant challenges. Existing search algorithms in the literature fail to fully utilize prior knowledge to improve search efficiency, and exhibit si
Human-robot interaction plays a critical role in scientific experiments by ensuring efficient and reliable execution of experimental tasks. To achieve successful task completion, robots must adapt in real time to unexpected task variations, external disturbances, and safety constraints. In this work
Reactive planning enables the robots to deal with dynamic events in uncertain environments. However, existing methods heavily rely on the predefined hard-coded robot behaviors, e.g, a pre-coded temporal logic formula that specifies how robot should react. Little attention has been paid for autonomou…
It is challenging to coordinately allocate and plan the tasks of a heterogeneous multi-agent system in a shared workspace. It can be even more challenging if the agents are subject to limited communication capability (i.e., exchange information with nearby agents only) and complex tasks with tempora
Reinforcement Learning (RL) based methods have been increasingly explored for robot learning. However, RL based methods often suffer from low sampling efficiency in the exploration phase, especially for long-horizon manipulation tasks, and generally neglect the semantic information from the task lev
This work develops a fast mission planning framework named planning decision tree (PDT), that can handle large-scale multi-robot systems with temporal logic specifications in real time. Specifically, PDT builds a tree incrementally to represent the task progress. The system states are modeled by bot
Empowering legged robots with agile maneuvers is a great challenge. While existing works have proposed diverse control-based and learning-based methods, it remains an open problem to endow robots with animal-like perception and athleticism. Towards this goal, we develop an End-to-End Legged Percepti…
"While recent low-light image enhancement (LLIE) methods have made significant advancements, they still face challenges in terms of low visual quality and weak generalization ability when applied to complex scenarios. To address these issues, we propose a semi-supervised method based on latent mean-…
Visual-based robot pose estimation is a fundamental challenge, involving the determination of the camera’s pose with respect to a robot. Conventional methods for camera-to-robot pose calibration rely on fiducial markers to establish keypoint correspondences. However, these approaches exhibit signifi…
While reinforcement learning (RL) attracts increasing research attention, maximizing the return while keeping the agent safe at the same time remains an open problem. Motivated to address this challenge, this work proposes a new Fast and Safe Policy Optimization (FSPO) algorithm, which consists of t…
This works exploits soft constraints in linear temporal logic task planning to enhance the agent’s capability in handling potentially conflicting or even infeasible tasks. Different from most existing works that focus on sticking to the original plan and trying to find a relaxed plan if the workspac…
Fast motion planning is of great significance, espe-cially when a timely mission is desired. However, the complexity of motion planning can grow drastically with the increase of environment details and mission complexity. This challenge can be further exacerbated if the tasks are coupled with the de…
Automaton based approaches have enabled robots to perform various complex tasks. However, most existing automaton based algorithms highly rely on the manually customized representation of states for the considered task, limiting its applicability in deep reinforcement learning algorithms. To address
This work develops a fast task allocation framework for heterogeneous multi-robot systems subject to both temporal logic and inter-task constraints. The considered inter-task constraints include unrelated tasks, compatible tasks, and exclusive tasks. To specify such inter-task relationships, we exte
Learning-based path planning is becoming a promising robot navigation methodology due to its adaptability to various environments. However, the expensive computing and storage associated with networks impose significant challenges for their deployment on low-cost robots. Motivated by this practical…
Transparent objects are widely used in industrial automation and daily life. However, robust visual recognition and perception of transparent objects have always been a major challenge. Currently, most commercial-grade depth cameras are still not good at sensing the surfaces of transparent objects d…
The ability of human operators in estimating the partners’ motion intention and utilizing it for collaboration brings valuable enlightenment to human-robot systems. Motivated by these observations, this letter introduces an assimilation control method that reshapes the physical interaction trajector
Reinforcement learning (RL) based approaches have enabled robots to perform various tasks. However, most existing RL algorithms focus on learning a particular task, without considering generalization to new tasks. To address this issue, by combining meta learning and reinforcement learning, we devel
Dynamic movement primitives (DMPs) are a flexible trajectory learning scheme widely used in motion generation of robotic systems. However, existing DMP-based methods mainly focus on simple go-to-goal tasks. Motivated to handle tasks beyond point-to-point motion planning, this work presents temporal…
In this letter, we present a transformer-based architecture, namely TF-Grasp, for robotic grasp detection. The developed TF-Grasp framework has two elaborate designs making it well suitable for visual grasping tasks. The first key design is that we adopt the local window attention to capture local c
This letter investigates the motion planning of autonomous dynamical systems modeled by Markov decision processes (MDP) with unknown transition probabilities over continuous state and action spaces. Linear temporal logic (LTL) is used to specify high-level tasks over infinite horizon, which can be c
This paper presents a model-free reinforcement learning (RL) algorithm to synthesize a control policy that maximizes the satisfaction probability of complex tasks, which are expressed by linear temporal logic (LTL) specifications. Due to the consideration of environment and motion uncertainties, we…