14 citations · 44 across the 15 of their papers we have counts for
11 papers · 1 filter
Learning Real-World Action-Video Dynamics with Heterogeneous Masked Autoregression
Lirui Wang, Kevin Zhao, Chaoqi Liu +1
We propose Heterogeneous Masked Autoregression (HMA) for modeling action-video dynamics to generate high-quality data and evaluation in scaling robot learning. Building interactive…
Inference-Time Policy Steering through Human Interactions
Yanwei Wang, Lirui Wang, Yilun Du +6
Generative policies trained with human demonstrations can autonomously accomplish multimodal, long-horizon tasks. However, during inference, humans are often removed from the polic…
GenSim2: Scaling Robot Data Generation with Multi-modal and Reasoning LLMs
Pu Hua, Minghuan Liu, Annabella Macaluso +4
Robotic simulation today remains challenging to scale up due to the human efforts required to create diverse simulation tasks and scenes. Simulation-trained policies also face scal…
Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers
Lirui Wang, Xinlei Chen, Jialiang Zhao +1
One of the roadblocks for training generalist robotic models today is heterogeneity. Previous robot learning methods often collect data to train with one specific embodiment for on…
Transferable Tactile Transformers for Representation Learning Across Diverse Sensors and Tasks
Jialiang Zhao, Yuxiang Ma, Lirui Wang +1
This paper presents T3: Transferable Tactile Transformers, a framework for tactile representation learning that scales across multi-sensors and multi-tasks. T3 is designed to overc…
PoCo: Policy Composition from and for Heterogeneous Robot Learning
Lirui Wang, Jialiang Zhao, Yilun Du +2
Training general robotic policies from heterogeneous data for different tasks is a significant challenge. Existing robotic datasets vary in different modalities such as color, dept…
Robot Fleet Learning via Policy Merging
Lirui Wang, Kaiqing Zhang, Allan Zhou +2
Fleets of robots ingest massive amounts of heterogeneous streaming data silos generated by interacting with their environments, far more than what can be stored or transmitted with…
PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulation
Alexey Kamenev, Lirui Wang, Ollin Boer Bohan +6
Predicting the future motion of traffic agents is crucial for safe and efficient autonomous driving. To this end, we present PredictionNet, a deep neural network (DNN) that predict…
Hierarchical Policies for Cluttered-Scene Grasping with Latent Plans
Lirui Wang, Xiangyun Meng, Yu Xiang +1
6D grasping in cluttered scenes is a longstanding problem in robotic manipulation. Open-loop manipulation pipelines may fail due to inaccurate state estimation, while most end-to-e…
Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds
Lirui Wang, Yu Xiang, Wei Yang +2
6D robotic grasping beyond top-down bin-picking scenarios is a challenging task. Previous solutions based on 6D grasp synthesis with robot motion planning usually operate in an ope…
Manipulation Trajectory Optimization with Online Grasp Synthesis and Selection
Lirui Wang, Yu Xiang, Dieter Fox
In robot manipulation, planning the motion of a robot manipulator to grasp an object is a fundamental problem. A manipulation planner needs to generate a trajectory of the manipula…