6 papers · 1 filter
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…
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…
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…
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…
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…
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…