354 citations · 1.1k across the 115 of their papers we have counts for
30 papers · 1 filter
RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
Charles Xu, Qiyang Li, Jianlan Luo +1
Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, th…
DrivingRecon: Large 4D Gaussian Reconstruction Model For Autonomous Driving
Hao Lu, Tianshuo Xu, Wenzhao Zheng +6
Photorealistic 4D reconstruction of street scenes is essential for developing real-world simulators in autonomous driving. However, most existing methods perform this task offline…
Driv3R: Learning Dense 4D Reconstruction for Autonomous Driving
Xin Fei, Wenzhao Zheng, Yueqi Duan +4
Realtime 4D reconstruction for dynamic scenes remains a crucial challenge for autonomous driving perception. Most existing methods rely on depth estimation through self-supervision…
X-Drive: Cross-modality consistent multi-sensor data synthesis for driving scenarios
Yichen Xie, Chenfeng Xu, Chensheng Peng +6
Recent advancements have exploited diffusion models for the synthesis of either LiDAR point clouds or camera image data in driving scenarios. Despite their success in modeling sing…
DexHandDiff: Interaction-aware Diffusion Planning for Adaptive Dexterous Manipulation
Zhixuan Liang, Yao Mu, Yixiao Wang +6
Dexterous manipulation with contact-rich interactions is crucial for advanced robotics. While recent diffusion-based planning approaches show promise for simple manipulation tasks,…
DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes
Chensheng Peng, Chengwei Zhang, Yixiao Wang +6
We present DeSiRe-GS, a self-supervised gaussian splatting representation, enabling effective static-dynamic decomposition and high-fidelity surface reconstruction in complex drivi…
CompGS: Unleashing 2D Compositionality for Compositional Text-to-3D via Dynamically Optimizing 3D Gaussians
Chongjian Ge, Chenfeng Xu, Yuanfeng Ji +6
Recent breakthroughs in text-guided image generation have significantly advanced the field of 3D generation. While generating a single high-quality 3D object is now feasible, gener…
PixelGaussian: Generalizable 3D Gaussian Reconstruction from Arbitrary Views
Xin Fei, Wenzhao Zheng, Yueqi Duan +4
We propose PixelGaussian, an efficient feed-forward framework for learning generalizable 3D Gaussian reconstruction from arbitrary views. Most existing methods rely on uniform pixe…
Language-Driven Policy Distillation for Cooperative Driving in Multi-Agent Reinforcement Learning
Jiaqi Liu, Chengkai Xu, Peng Hang +4
The cooperative driving technology of Connected and Autonomous Vehicles (CAVs) is crucial for improving the efficiency and safety of transportation systems. Learning-based methods,…
TrajSSL: Trajectory-Enhanced Semi-Supervised 3D Object Detection
Philip Jacobson, Yichen Xie, Mingyu Ding +4
Semi-supervised 3D object detection is a common strategy employed to circumvent the challenge of manually labeling large-scale autonomous driving perception datasets. Pseudo-labeli…
Embodiment-Agnostic Action Planning via Object-Part Scene Flow
Weiliang Tang, Jia-Hui Pan, Wei Zhan +6
Observing that the key for robotic action planning is to understand the target-object motion when its associated part is manipulated by the end effector, we propose to generate the…
P2 Explore: Efficient Exploration in Unknown Cluttered Environment with Floor Plan Prediction
Kun Song, Gaoming Chen, Masayoshi Tomizuka +3
Robot exploration aims at the reconstruction of unknown environments, and it is important to achieve it with shorter paths. Traditional methods focus on optimizing the visiting ord…
DSLO: Deep Sequence LiDAR Odometry Based on Inconsistent Spatio-temporal Propagation
Huixin Zhang, Guangming Wang, Xinrui Wu +5
This paper introduces a 3D point cloud sequence learning model based on inconsistent spatio-temporal propagation for LiDAR odometry, termed DSLO. It consists of a pyramid structure…
Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation
Yixiao Wang, Chen Tang, Lingfeng Sun +8
Diffusion models are promising for joint trajectory prediction and controllable generation in autonomous driving, but they face challenges of inefficient inference steps and high c…
WOMD-Reasoning: A Large-Scale Dataset for Interaction Reasoning in Driving
Yiheng Li, Cunxin Fan, Chongjian Ge +9
Language models uncover unprecedented abilities in analyzing driving scenarios, owing to their limitless knowledge accumulated from text-based pre-training. Naturally, they should…
Sparse Diffusion Policy: A Sparse, Reusable, and Flexible Policy for Robot Learning
Yixiao Wang, Yifei Zhang, Mingxiao Huo +8
The increasing complexity of tasks in robotics demands efficient strategies for multitask and continual learning. Traditional models typically rely on a universal policy for all ta…
Residual-MPPI: Online Policy Customization for Continuous Control
Pengcheng Wang, Chenran Li, Catherine Weaver +4
Policies developed through Reinforcement Learning (RL) and Imitation Learning (IL) have shown great potential in continuous control tasks, but real-world applications often require…
MEReQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Intervention
Yuxin Chen, Chen Tang, Jianglan Wei +6
Aligning robot behavior with human preferences is crucial for deploying embodied AI agents in human-centered environments. A promising solution is interactive imitation learning fr…
Gaussian: Self-Supervised Street Gaussians for Autonomous Driving
Nan Huang, Xiaobao Wei, Wenzhao Zheng +6
Photorealistic 3D reconstruction of street scenes is a critical technique for developing real-world simulators for autonomous driving. Despite the efficacy of Neural Radiance Field…
NeRFs in Robotics: A Survey
Guangming Wang, Lei Pan, Songyou Peng +7
Detailed and realistic 3D environment representations have been a long-standing goal in the fields of computer vision and robotics. The recent emergence of neural implicit represen…
Efficient Reinforcement Learning of Task Planners for Robotic Palletization through Iterative Action Masking Learning
Zheng Wu, Yichuan Li, Wei Zhan +3
The development of robotic systems for palletization in logistics scenarios is of paramount importance, addressing critical efficiency and precision demands in supply chain managem…
Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach
Juanwu Lu, Wei Zhan, Masayoshi Tomizuka +1
Estimating the potential behavior of the surrounding human-driven vehicles is crucial for the safety of autonomous vehicles in a mixed traffic flow. Recent state-of-the-art achieve…
Q-SLAM: Quadric Representations for Monocular SLAM
Chensheng Peng, Chenfeng Xu, Yue Wang +6
In this paper, we reimagine volumetric representations through the lens of quadrics. We posit that rigid scene components can be effectively decomposed into quadric surfaces. Lever…
DrPlanner: Diagnosis and Repair of Motion Planners for Automated Vehicles Using Large Language Models
Yuanfei Lin, Chenran Li, Mingyu Ding +3
Motion planners are essential for the safe operation of automated vehicles across various scenarios. However, no motion planning algorithm has achieved perfection in the literature…