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Found 128 papers, 30 papers with code
The Feasibility of Constrained Reinforcement Learning Algorithms: A Tutorial Study
We demonstrate our feasibility theory by visualizing different feasible regions under both MPC and RL policies in an emergency braking control task.
RoadBEV: Road Surface Reconstruction in Bird's Eye View
This paper uniformly proposes two simple yet effective models for road elevation reconstruction in BEV named RoadBEV-mono and RoadBEV-stereo, which estimate road elevation with monocular and stereo images, respectively.
PNAS-MOT: Multi-Modal Object Tracking with Pareto Neural Architecture Search
Multiple object tracking is a critical task in autonomous driving.
Q-SLAM: Quadric Representations for Monocular SLAM
This focus results in a significant disconnect between NeRF applications, i. e., novel-view synthesis and the requirements of SLAM.
Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach
For interpretability, the model achieves target-driven motion prediction by estimating the spatial distribution of long-term destinations with a variational mixture of Gaussians.
MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts
Data-driven methods have great advantages in modeling complicated human behavioral dynamics and dealing with many human-robot interaction applications.
PhyGrasp: Generalizing Robotic Grasping with Physics-informed Large Multimodal Models
With these two capabilities, PhyGrasp is able to accurately assess the physical properties of object parts and determine optimal grasping poses.
Cohere3D: Exploiting Temporal Coherence for Unsupervised Representation Learning of Vision-based Autonomous Driving
Observations from different angles enable the recovery of 3D object states from 2D image inputs if we can identify the same instance in different input frames.
BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay
Thus, we propose BeTAIL: Behavior Transformer Adversarial Imitation Learning, which combines a Behavior Transformer (BeT) policy from human demonstrations with online AIL.
Depth-aware Volume Attention for Texture-less Stereo Matching
Furthermore, we propose a more rigorous evaluation metric that considers depth-wise relative error, providing comprehensive evaluations for universal stereo matching and depth estimation models.
Controllable Safety-Critical Closed-loop Traffic Simulation via Guided Diffusion
These findings affirm that guided diffusion models provide a robust and versatile foundation for safety-critical, interactive traffic simulation, extending their utility across the broader landscape of autonomous driving.
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.
Multi-level Reasoning for Robotic Assembly: From Sequence Inference to Contact Selection
Automating the assembly of objects from their parts is a complex problem with innumerable applications in manufacturing, maintenance, and recycling.
Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks
Designing robotic agents to perform open vocabulary tasks has been the long-standing goal in robotics and AI.
Active Exploration in Iterative Gaussian Process Regression for Uncertainty Modeling in Autonomous Racing
In the exploration, a MPC collects diverse data by balancing the racing objectives and the exploration criterion; then the GP is re-trained.
What Matters to You? Towards Visual Representation Alignment for Robot Learning
When operating in service of people, robots need to optimize rewards aligned with end-user preferences.
Quantifying Agent Interaction in Multi-agent Reinforcement Learning for Cost-efficient Generalization
We observe that, generally, a more diverse set of co-play agents during training enhances the generalization performance of the ego agent; however, this improvement varies across distinct scenarios and environments.
Human-oriented Representation Learning for Robotic Manipulation
We introduce Task Fusion Decoder as a plug-and-play embedding translator that utilizes the underlying relationships among these perceptual skills to guide the representation learning towards encoding meaningful structure for what's important for all perceptual skills, ultimately empowering learning of downstream robotic manipulation tasks.
LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving
Existing learning-based autonomous driving (AD) systems face challenges in comprehending high-level information, generalizing to rare events, and providing interpretability.
Generalizable Long-Horizon Manipulations with Large Language Models
This work introduces a framework harnessing the capabilities of Large Language Models (LLMs) to generate primitive task conditions for generalizable long-horizon manipulations with novel objects and unseen tasks.
RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Driving
This paper addresses the growing demands for safety and comfort in intelligent robot systems, particularly autonomous vehicles, where road conditions play a pivotal role in overall driving performance.
Towards Free Data Selection with General-Purpose Models
However, current approaches, represented by active learning methods, typically follow a cumbersome pipeline that iterates the time-consuming model training and batch data selection repeatedly.
Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration
GOLD distills an offline DT policy into a lightweight policy network through guided online safe RL training, which outperforms both the offline DT policy and online safe RL algorithms.
Pre-training on Synthetic Driving Data for Trajectory Prediction
We propose to augment both HD maps and trajectories and apply pre-training strategies on top of them.
DELFlow: Dense Efficient Learning of Scene Flow for Large-Scale Point Clouds
Previous methods rarely predict scene flow from the entire point clouds of the scene with one-time inference due to the memory inefficiency and heavy overhead from distance calculation and sorting involved in commonly used farthest point sampling, KNN, and ball query algorithms for local feature aggregation.
NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detection
We present NeRF-Det, a novel method for indoor 3D detection with posed RGB images as input.
An Efficient General-Purpose Modular Vision Model via Multi-Task Heterogeneous Training
We present a model that can perform multiple vision tasks and can be adapted to other downstream tasks efficiently.
Residual Q-Learning: Offline and Online Policy Customization without Value
Specifically, we formulate the customization problem as a Markov Decision Process (MDP) with a reward function that combines 1) the inherent reward of the demonstration; and 2) the add-on reward specified by the downstream task.
Skill-Critic: Refining Learned Skills for Reinforcement Learning
Hierarchical reinforcement learning (RL) can accelerate long-horizon decision-making by temporally abstracting a policy into multiple levels.
Efficient Multi-Task and Transfer Reinforcement Learning with Parameter-Compositional Framework
In this work, we investigate the potential of improving multi-task training and also leveraging it for transferring in the reinforcement learning setting.
Quadric Representations for LiDAR Odometry, Mapping and Localization
Current LiDAR odometry, mapping and localization methods leverage point-wise representations of 3D scenes and achieve high accuracy in autonomous driving tasks.
SparseFusion: Fusing Multi-Modal Sparse Representations for Multi-Sensor 3D Object Detection
However, given that objects occupy only a small part of a scene, finding dense candidates and generating dense representations is noisy and inefficient.
Open-Vocabulary Point-Cloud Object Detection without 3D Annotation
In this paper, we address open-vocabulary 3D point-cloud detection by a dividing-and-conquering strategy, which involves: 1) developing a point-cloud detector that can learn a general representation for localizing various objects, and 2) connecting textual and point-cloud representations to enable the detector to classify novel object categories based on text prompting.
Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm
We propose a novel method called ActiveFT for active finetuning task to select a subset of data distributing similarly with the entire unlabeled pool and maintaining enough diversity by optimizing a parametric model in the continuous space.
Editing Driver Character: Socially-Controllable Behavior Generation for Interactive Traffic Simulation
To ensure that autonomous vehicles take safe and efficient maneuvers in different interactive traffic scenarios, we should be able to evaluate autonomous vehicles against reactive agents with different social characteristics in the simulation environment.
UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling
Particularly, on the MSRVTT retrieval task, UniAdapter achieves 49. 7% recall@1 with 2. 2% model parameters, outperforming the latest competitors by 2. 0%.
AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
For example, AdaptDiffuser not only outperforms the previous art Diffuser by 20. 8% on Maze2D and 7. 5% on MuJoCo locomotion, but also adapts better to new tasks, e. g., KUKA pick-and-place, by 27. 9% without requiring additional expert data.
Towards Modeling and Influencing the Dynamics of Human Learning
Interestingly, robot actions influence what this experience is, and therefore influence how people's internal models change.
PaCo: Parameter-Compositional Multi-Task Reinforcement Learning
However, the gaps between contents and difficulties of different tasks bring us challenges on both which tasks should share the parameters and what parameters should be shared, as well as the optimization challenges due to parameter sharing.
Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection
While recent camera-only 3D detection methods leverage multiple timesteps, the limited history they use significantly hampers the extent to which temporal fusion can improve object perception.
Ranked #1 on
Robust Camera Only 3D Object Detection
on nuScenes-C
Zero-Shot Policy Transfer with Disentangled Task Representation of Meta-Reinforcement Learning
Experimental results demonstrate that our proposed method achieves policy generalization to unseen compositional tasks in a zero-shot manner.
Center Feature Fusion: Selective Multi-Sensor Fusion of Center-based Objects
In this work, we propose a novel approach Center Feature Fusion (CFF), in which we leverage center-based detection networks in both the camera and LiDAR streams to identify relevant object locations.
Analyzing and Enhancing Closed-loop Stability in Reactive Simulation
In this paper, we aim to provide a thorough stability analysis of the reactive simulation and propose a solution to enhance the stability.
Generalizability Analysis of Graph-based Trajectory Predictor with Vectorized Representation
Results show significant performance degradation due to domain shift, and feature attribution provides insights to identify potential causes of these problems.
What Matters for 3D Scene Flow Network
Our proposed model surpasses all existing methods by at least 38. 2% on FlyingThings3D dataset and 24. 7% on KITTI Scene Flow dataset for EPE3D metric.
SST-Calib: Simultaneous Spatial-Temporal Parameter Calibration between LIDAR and Camera
With information from multiple input modalities, sensor fusion-based algorithms usually out-perform their single-modality counterparts in robotics.
Open-Vocabulary 3D Detection via Image-level Class and Debiased Cross-modal Contrastive Learning
Current point-cloud detection methods have difficulty detecting the open-vocabulary objects in the real world, due to their limited generalization capability.
PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map
It is hard to replicate these approaches in trajectory forecasting due to the lack of adequate trajectory data (e. g., 34K samples in the nuScenes dataset).
Interventional Behavior Prediction: Avoiding Overly Confident Anticipation in Interactive Prediction
Moreover, to properly evaluate an IBP model with offline datasets, we propose a Shapley-value-based metric to verify if the prediction model satisfies the inherent temporal independence of an interventional distribution.
Learning to Synthesize Volumetric Meshes from Vision-based Tactile Imprints
Vision-based tactile sensors typically utilize a deformable elastomer and a camera mounted above to provide high-resolution image observations of contacts.
Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory Prediction
To address these issues, we propose a novel approach to avoid KL vanishing and induce an interpretable interactive latent space with pseudo labels.
DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object Detection
While numerous 3D detection works leverage the complementary relationship between RGB images and point clouds, developments in the broader framework of semi-supervised object recognition remain uninfluenced by multi-modal fusion.
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.
Transferable and Adaptable Driving Behavior Prediction
By mimicking humans' cognition model and semantic understanding during driving, we propose HATN, a hierarchical framework to generate high-quality, transferable, and adaptable predictions for driving behaviors in multi-agent dense-traffic environments.
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.
Towards General and Efficient Active Learning
Existing work follows a cumbersome pipeline that repeats the time-consuming model training and batch data selection multiple times.
Causal-based Time Series Domain Generalization for Vehicle Intention Prediction
In this paper, we aim to address the domain generalization problem for vehicle intention prediction tasks and a causal-based time series domain generalization (CTSDG) model is proposed.
Exploring Social Posterior Collapse in Variational Autoencoder for Interaction Modeling
In this work, we argue that one of the typical formulations of VAEs in multi-agent modeling suffers from an issue we refer to as social posterior collapse, i. e., the model is prone to ignoring historical social context when predicting the future trajectory of an agent.
Dealing with the Unknown: Pessimistic Offline Reinforcement Learning
Reinforcement Learning (RL) has been shown effective in domains where the agent can learn policies by actively interacting with its operating environment.
Grouptron: Dynamic Multi-Scale Graph Convolutional Networks for Group-Aware Dense Crowd Trajectory Forecasting
Accurate, long-term forecasting of pedestrian trajectories in highly dynamic and interactive scenes is a long-standing challenge.
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.
Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models
We discover that we can indeed use the same architecture and pretrained weights of a neural net model to understand both images and point-clouds.
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.
History Encoding Representation Design for Human Intention Inference
In this extended abstract, we investigate the design of learning representation for human intention inference.
Convex Parameterization and Optimization for Robust Tracking of a Magnetically Levitated Planar Positioning System
Magnetic levitation positioning technology has attracted considerable research efforts and dedicated attention due to its extremely attractive features.
Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Data
In this work, we advocate that humans are bounded rational and have different intelligence levels when reasoning about others' decision-making process, and such an inherent and latent characteristic should be accounted for in reward learning algorithms.
A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding
Detecting dynamic objects and predicting static road information such as drivable areas and ground heights are crucial for safe autonomous driving.
Diverse Critical Interaction Generation for Planning and Planner Evaluation
Experiments show that our model can generate diverse interactions in various scenarios.
Grounded Relational Inference: Domain Knowledge Driven Explainable Autonomous Driving
Explainability is essential for autonomous vehicles and other robotics systems interacting with humans and other objects during operation.
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.
Contact Pose Identification for Peg-in-Hole Assembly under Uncertainties
Peg-in-hole assembly is a challenging contact-rich manipulation task.
Robotics
A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning
To address this challenge, we propose a hierarchical behavior planning framework with a set of low-level safe controllers and a high-level reinforcement learning algorithm (H-CtRL) as a coordinator for the low-level controllers.
Visual Transformers: Where Do Transformers Really Belong in Vision Models?
A recent trend in computer vision is to replace convolutions with transformers.
Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection
As a result, an in-depth evaluation among different object detection methods remains challenging, and the training process of object detectors is sub-optimal, especially in probabilistic object detection.
Sparse R-CNN: End-to-End Object Detection with Learnable Proposals
In our method, however, a fixed sparse set of learned object proposals, total length of $N$, are provided to object recognition head to perform classification and location.
Ranked #5 on
2D Object Detection
on CeyMo
COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing
General contact-rich manipulation problems are long-standing challenges in robotics due to the difficulty of understanding complicated contact physics.
IDE-Net: Interactive Driving Event and Pattern Extraction from Human Data
We find three interpretable patterns of interactions, bringing insights for driver behavior representation, modeling and comprehension.
Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data
It allows the AVs to infer the characteristics of other road users online and generate behaviors optimizing not only their own rewards, but also their courtesy to others, and their confidence regarding the prediction uncertainties.
Bounded Risk-Sensitive Markov Games: Forward Policy Design and Inverse Reward Learning with Iterative Reasoning and Cumulative Prospect Theory
Classical game-theoretic approaches for multi-agent systems in both the forward policy design problem and the inverse reward learning problem often make strong rationality assumptions: agents perfectly maximize expected utilities under uncertainties.
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.
Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving
Different from existing IRL algorithms, by introducing an efficient continuous-domain trajectory sampler, the proposed algorithm can directly learn the reward functions in the continuous domain while considering the uncertainties in demonstrated trajectories from human drivers.
Towards Better Performance and More Explainable Uncertainty for 3D Object Detection of Autonomous Vehicles
In this paper, we propose a novel form of the loss function to increase the performance of LiDAR-based 3d object detection and obtain more explainable and convincing uncertainty for the prediction.
In Proximity of ReLU DNN, PWA Function, and Explicit MPC
Rectifier (ReLU) deep neural networks (DNN) and their connection with piecewise affine (PWA) functions is analyzed.
Visual Transformers: Token-based Image Representation and Processing for Computer Vision
In this work, we challenge this paradigm by (a) representing images as semantic visual tokens and (b) running transformers to densely model token relationships.
Scenario-Transferable Semantic Graph Reasoning for Interaction-Aware Probabilistic Prediction
Accurately predicting the possible behaviors of traffic participants is an essential capability for autonomous vehicles.
SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation
Using standard convolutions to process such LiDAR images is problematic, as convolution filters pick up local features that are only active in specific regions in the image.
Ranked #24 on
3D Semantic Segmentation
on SemanticKITTI
EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning
In this paper, we propose a generic trajectory forecasting framework (named EvolveGraph) with explicit relational structure recognition and prediction via latent interaction graphs among multiple heterogeneous, interactive agents.
Ranked #12 on
Trajectory Prediction
on Stanford Drone
End-to-end Autonomous Driving Perception with Sequential Latent Representation Learning
Current autonomous driving systems are composed of a perception system and a decision system.
Inferring Spatial Uncertainty in Object Detection
Based on the spatial distribution, we further propose an extension of IoU, called the Jaccard IoU (JIoU), as a new evaluation metric that incorporates label uncertainty.
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.
Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning
A sequential latent environment model is introduced and learned jointly with the reinforcement learning process.
AutoScale: Learning to Scale for Crowd Counting and Localization
A major issue is that the density map on dense regions usually accumulates density values from a number of nearby Gaussian blobs, yielding different large density values on a small set of pixels.
UrbanLoco: A Full Sensor Suite Dataset for Mapping and Localization in Urban Scenes
Mapping and localization is a critical module of autonomous driving, and significant achievements have been reached in this field.
Robust Feature-Based Point Registration Using Directional Mixture Model
This paper presents a robust probabilistic point registration method for estimating the rigid transformation (i. e. rotation matrix and translation vector) between two pointcloud dataset.
Adaptive Probabilistic Vehicle Trajectory Prediction Through Physically Feasible Bayesian Recurrent Neural Network
Current methods for long-term trajectory prediction cannot guarantee the physical feasibility of predicted distribution.
Online Learning in Planar Pushing with Combined Prediction Model
In this paper, we propose a combined prediction model and an online learning framework for planar push prediction.
INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps
3) The driving behavior is highly interactive and complex with adversarial and cooperative motions of various traffic participants.
epBRM: Improving a Quality of 3D Object Detection using End Point Box Regression Module
Our approach can improve a 3D object detection performance by predicting more precise 3D bounding box coordinates.
Generic Tracking and Probabilistic Prediction Framework and Its Application in Autonomous Driving
The framework can incorporate an arbitrary prediction model as the implicit proposal distribution of the CMSMC method.
Generic Prediction Architecture Considering both Rational and Irrational Driving Behaviors
Both rational and irrational behaviors exist, and the autonomous vehicles need to be aware of this in their prediction module.
Interpretable Modelling of Driving Behaviors in Interactive Driving Scenarios based on Cumulative Prospect Theory
Hence, the goal of this work is to formulate the human drivers' behavior generation model with CPT so that some ``irrational'' behavior or decisions of human can be better captured and predicted.
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
Behavior Planning of Autonomous Cars with Social Perception
The uncertainties can come either from sensor limitations such as occlusions and limited sensor range, or from probabilistic prediction of other road participants, or from unknown social behavior in a new area.
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.
Model-free Deep Reinforcement Learning for Urban Autonomous Driving
Urban autonomous driving decision making is challenging due to complex road geometry and multi-agent interactions.
Optimization Model for Planning Precision Grasps with Multi-Fingered Hands
Searching precision grasps on the object represented by point cloud, is challenging due to the complex object shape, high-dimensionality, collision and undesired properties of the sensing and positioning.
Robotics
Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarios
In this paper, we proposed an interaction-aware decision making with adaptive strategies (IDAS) approach that can let the autonomous vehicle negotiate the road with other drivers by leveraging their cooperativeness under merging scenarios.
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.
Multi-modal Probabilistic Prediction of Interactive Behavior via an Interpretable Model
The proposed method is based on a generative model and is capable of jointly predicting sequential motions of each pair of interacting agents.
Efficient Grasp Planning and Execution with Multi-Fingered Hands by Surface Fitting
The framework includes a multi-dimensional iterative surface fitting (MDISF) for grasp planning and a grasp trajectory optimization (GTO) for grasp imagination.
Robotics
Zero-shot Deep Reinforcement Learning Driving Policy Transfer for Autonomous Vehicles based on Robust Control
Although deep reinforcement learning (deep RL) methods have lots of strengths that are favorable if applied to autonomous driving, real deep RL applications in autonomous driving have been slowed down by the modeling gap between the source (training) domain and the target (deployment) domain.
RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement
We present RoarNet, a new approach for 3D object detection from a 2D image and 3D Lidar point clouds.
Ranked #3 on
Object Detection
on KITTI Cars Easy
A Framework for Probabilistic Generic Traffic Scene Prediction
In a given scenario, simultaneously and accurately predicting every possible interaction of traffic participants is an important capability for autonomous vehicles.
A Learning Framework for Robust Bin Picking by Customized Grippers
In this paper, we propose a learning framework to plan robust grasps for customized grippers in real-time.
A Learning Framework for High Precision Industrial Assembly
The framework combines both the supervised learning and the reinforcement learning.
Towards a Fatality-Aware Benchmark of Probabilistic Reaction Prediction in Highly Interactive Driving Scenarios
Modified methods based on PGM, NN and IRL are provided to generate probabilistic reaction predictions in an exemplar scenario of nudging from a highway ramp.
Probabilistic Prediction of Interactive Driving Behavior via Hierarchical Inverse Reinforcement Learning
To safely and efficiently interact with other road participants, AVs have to accurately predict the behavior of surrounding vehicles and plan accordingly.
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.
Robot Safe Interaction System for Intelligent Industrial Co-Robots
Human-robot interactions have been recognized to be a key element of future industrial collaborative robots (co-robots).
Robotics Systems and Control
Courteous Autonomous Cars
Such a courtesy term enables the robot car to be aware of possible irrationality of the human behavior, and plan accordingly.
Probabilistic Prediction of Vehicle Semantic Intention and Motion
Accurately predicting the possible behaviors of traffic participants is an essential capability for future autonomous vehicles.
Grasp Planning for Customized Grippers by Iterative Surface Fitting
The proposed algorithm is able to consider the structural constraints of the gripper and plan optimal grasps in real-time.
Robotics
Fusing Bird View LIDAR Point Cloud and Front View Camera Image for Deep Object Detection
The fusion method shows particular benefit for detection of pedestrians in the bird view compared to other fusion-based object detection networks.
The Convex Feasible Set Algorithm for Real Time Optimization in Motion Planning
The idea is to find a convex feasible set for the original problem and iteratively solve a sequence of subproblems using the convex constraints.
Optimization and Control Robotics
A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning
For safe and efficient planning and control in autonomous driving, we need a driving policy which can achieve desirable driving quality in long-term horizon with guaranteed safety and feasibility.
