29 accepted papers
This paper presents a novel shared autonomy and baseline policy adapting framework for human-robot interactions in high-level context-aware robotic tasks. With a unique methodology that leverages hierarchies in decision-making as well as variational analysis of human policy, we propose a mathematica…
Diffusion-based models have recently shown strong performance in trajectory planning, as they are capable of capturing the diverse, multi-modal distributions of complex behaviors. A key limitation of these models, however, is their slow inference speed due to the iterative denoising process. This ma…
This letter addresses the challenge of robot follow ahead applications where the human behavior is highly variable. We propose a novel approach that does not rely on single human trajectory prediction but instead considers multiple potential future positions of the human, along with their associated
Detecting human actions is a crucial task for autonomous robots and vehicles, often requiring the integration of various data modalities for improved accuracy. In this study, we introduce a novel approach to Human Action Recognition (HAR) using language supervision named LS-HAR based on skeleton and
Deep Reinforcement Learning (RL) has shown remarkable success in robotics with complex and heterogeneous dynamics. However, its vulnerability to unknown disturbances and adversarial attacks remains a significant challenge. In this paper, we propose a robust policy training framework that integrates
In end-to-end autonomous driving, current sensor fusion and navigational control techniques used by imitation learning algorithms are insufficient in challenging scenarios involving multiple dynamic agents and result in poor driving capabilities. To tackle this issue, we introduce DMFuser, a transfo
Long-term human trajectory prediction is a challenging yet critical task in robotics and autonomous systems. Prior work that studied how to predict accurate short-term human trajectories with only unimodal features often failed in long-term prediction. Reinforcement learning provides a good solution…
The maritime industry's continuous commitment to sustainability has led to a dedicated exploration of methods to reduce vessel fuel consumption. This paper undertakes this challenge through a machine learning approach, leveraging a real-world dataset spanning two years of a passenger vessel in west…
We propose a novel methodology for robotic follow-ahead applications that address the critical challenge of obstacle and occlusion avoidance. Our approach effectively navigates the robot while ensuring avoidance of collisions and occlusions caused by surrounding objects. To achieve this, we develope…
Cooperative multi-agent problems often require coordination between agents, which can be achieved through a centralized policy that considers the global state. Multi-agent policy gradient (MAPG) methods are commonly used to learn such policies, but they are often limited to problems with low-level a…
Human body motion prediction is a fundamental part of many human-robot applications. Despite the recent progress in the area, most studies predict human body motion relative to a fixed joint and only limit their model to predict one possible future motion, or both. However, due to the complex nature…
Nowadays, transportation networks face the challenge of sub-optimal control policies that can have adverse effects on human health, the environment, and contribute to traffic congestion. Increased levels of air pollution and extended commute times caused by traffic bottlenecks make intersection traf…
Collaborative autonomous multi-agent systems covering a specified area have many potential applications. Traditional approaches for such problems involve designing model-based control policies; however, state-of-the-art classical control policy still exhibits a large degree of sub-optimality. We pre…
In this paper, we greatly expand the capability of robots to perform the follow-ahead task and variations of this task through development of a neural network model to predict future human motion from an observed human motion history. We propose a non-autoregressive transformer architecture to lever…
We present task-oriented Koopman-based control that utilizes end-to-end reinforcement learning and contrastive encoder to simultaneously learn the Koopman latent embedding, operator, and associated linear controller within an iterative loop. By prioritizing the task cost as the main objective for co…
Co-speech gestures are a principal component in conveying messages and enhancing interaction experiences between humans and critical ingredients in human-agent interaction, including virtual agents and robots. Existing machine learning approaches have yielded only marginal success in learning speech…
Although human navigational intent inference has been studied in the literature, none have adequately considered both the dynamics that describe human motion and internal human parameters that may affect human navigational behaviour. In this paper, we propose a general probabilistic framework to inf…
We propose a Visual Teach and Repeat (VTR) algorithm using semantic landmarks extracted from environmental objects for ground robots with fixed mount monocular cameras. The proposed algorithm is robust to changes in the starting pose of the camera/robot, where a pose is defined as the planar positio
Robots and artificial agents that interact with humans should be able to do so without bias and inequity, but facial perception systems have notoriously been found to work more poorly for certain groups of people than others. In our work, we aim to build a system that can perceive humans in a more t…
Understanding human navigational intent is essential for robots to be able to interact with and navigate around humans safely and naturally. Current methods typically perform inference through only one mode of perception such as human motion trajectory, and a single theoretical framework such as a l…
This paper investigates a hybrid solution which combines deep reinforcement learning (RL) and classical trajectory planning for the "following in front" application. Here, an autonomous robot aims to stay ahead of a person as the person freely walks around. Following in front is a challenging proble…
Safety is an important topic in autonomous driving since any collision may cause serious injury to people and damage to property. Hamilton-Jacobi (HJ) Reachability is a formal method that verifies safety in multi-agent interaction and provides a safety controller for collision avoidance. However, du…
Hamilton-Jacobi (HJ) reachability analysis is a powerful technique used to verify the safety of autonomous systems. HJ reachability is ideal for analysing nonlinear systems with disturbances and flexible set representations. A drawback to this approach is that it suffers from the curse of dimensiona…
This letter presents the Observation-based Least-Restrictive Collision Avoidance Module (OLR-CAM) that can be added to any autonomous robot working in a shared environment and provide a high-level safety layer to the existing policy for each robot. The OLR-CAM takes raw sensory observations as input
Model-free reinforcement learning (RL) is a powerful approach for learning control policies directly from high-dimensional state and observation. However, it tends to be data-inefficient, which is especially costly in robotic learning tasks. On the other hand, optimal control does not require data i…
Model-free Reinforcement Learning (RL) offers an attractive approach to learn control policies for high dimensional systems, but its relatively poor sample complexity often necessitates training in simulated environments. Even in simulation, goal-directed tasks whose natural reward function is spars…
Hamilton-Jacobi (HJ) reachability provides a flexible framework for the verification of safety in robotic systems: it accounts for nonlinear system dynamics and provides safety-preserving controllers. However, computational scalability limits its direct application to systems of less than five conti…
Reach-avoid problems involve driving a system to a set of desirable configurations while keeping it away from undesirable ones. Providing mathematical guarantees for such scenarios is challenging but have numerous potential practical applications. Due to the challenges, analysis of reach-avoid probl…
Hamilton-Jacobi (HJ) reachability is a method that provides rigorous analyses of the safety properties of dynamical systems. These guarantees can be provided by the computation of a backward reachable set (BRS), which represents the set of states from which the system may be driven into violating sa…