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Found 18 papers, 2 papers with code
CMP: Cooperative Motion Prediction with Multi-Agent Communication
Building on top of cooperative perception, this paper explores the feasibility and effectiveness of cooperative motion prediction.
Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation
In this paper, we propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures, and we demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
SkillDiffuser: Interpretable Hierarchical Planning via Skill Abstractions in Diffusion-Based Task Execution
Experiments on multi-task robotic manipulation benchmarks like Meta-World and LOReL demonstrate state-of-the-art performance and human-interpretable skill representations from SkillDiffuser.
Learning Physical Dynamics with Subequivariant Graph Neural Networks
Graph Neural Networks (GNNs) have become a prevailing tool for learning physical dynamics.
EvolveHypergraph: Group-Aware Dynamic Relational Reasoning for Trajectory Prediction
While the modeling of pair-wise relations has been widely studied in multi-agent interacting systems, its ability to capture higher-level and larger-scale group-wise activities is limited.
Important Object Identification with Semi-Supervised Learning for Autonomous Driving
We propose a novel approach for important object identification in egocentric driving scenarios with relational reasoning on the objects in the scene.
Learning Differentiable Safety-Critical Control using Control Barrier Functions for Generalization to Novel Environments
By embedding the optimization procedure of the exponential control barrier function based quadratic program (ECBF-QP) as a differentiable layer within a deep learning architecture, we propose a differentiable safety-critical control framework that enables generalization to new environments for high relative-degree systems with forward invariance guarantees.
Multi-Objective Diverse Human Motion Prediction With Knowledge Distillation
Obtaining accurate and diverse human motion prediction is essential to many industrial applications, especially robotics and autonomous driving.
Cross Domain Robot Imitation with Invariant Representation
In this paper, we consider CDIL on a class of similar robots.
RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting
Motion forecasting plays a significant role in various domains (e. g., autonomous driving, human-robot interaction), which aims to predict future motion sequences given a set of historical observations.
Spectral Temporal Graph Neural Network for Trajectory Prediction
To this end, we propose a Spectral Temporal Graph Neural Network (SpecTGNN), which can capture inter-agent correlations and temporal dependency simultaneously in frequency domain in addition to time domain.
Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking
Due to the existence of frequent interactions and uncertainty in the scene evolution, it is desired for the prediction system to enable relational reasoning on different entities and provide a distribution of future trajectories for each agent.
Expressing Diverse Human Driving Behavior with Probabilistic Rewards and Online Inference
In human-robot interaction (HRI) systems, such as autonomous vehicles, understanding and representing human behavior are important.
Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network
Effective understanding of the environment and accurate trajectory prediction of surrounding dynamic obstacles are indispensable for intelligent mobile systems (like autonomous vehicles and social robots) to achieve safe and high-quality planning when they navigate in highly interactive and crowded scenarios.
Conditional Generative Neural System for Probabilistic Trajectory Prediction
Effective understanding of the environment and accurate trajectory prediction of surrounding dynamic obstacles are critical for intelligent systems such as autonomous vehicles and wheeled mobile robotics navigating in complex scenarios to achieve safe and high-quality decision making, motion planning and control.
Ranked #14 on
Trajectory Prediction
on Stanford Drone
Coordination and Trajectory Prediction for Vehicle Interactions via Bayesian Generative Modeling
In order to tackle the task of probabilistic prediction for multiple, interactive entities, we propose a coordination and trajectory prediction system (CTPS), which has a hierarchical structure including a macro-level coordination recognition module and a micro-level subtle pattern prediction module which solves a probabilistic generation task.
Interaction-aware Multi-agent Tracking and Probabilistic Behavior Prediction via Adversarial Learning
In order to enable high-quality decision making and motion planning of intelligent systems such as robotics and autonomous vehicles, accurate probabilistic predictions for surrounding interactive objects is a crucial prerequisite.
Generic Probabilistic Interactive Situation Recognition and Prediction: From Virtual to Real
Accurate and robust recognition and prediction of traffic situation plays an important role in autonomous driving, which is a prerequisite for risk assessment and effective decision making.
