233 citations · 1.2k across the 86 of their papers we have counts for
103 papers
LiDAR-UDA: Self-ensembling Through Time for Unsupervised LiDAR Domain Adaptation
Amirreza Shaban, JoonHo Lee, Sanghun Jung +2
We introduce LiDAR-UDA, a novel two-stage self-training-based Unsupervised Domain Adaptation (UDA) method for LiDAR segmentation. Existing self-training methods use a model trained…
CAJun: Continuous Adaptive Jumping using a Learned Centroidal Controller
Yuxiang Yang, Guanya Shi, Xiangyun Meng +4
We present CAJun, a novel hierarchical learning and control framework that enables legged robots to jump continuously with adaptive jumping distances. CAJun consists of a high-leve…
Stackelberg Games for Learning Emergent Behaviors During Competitive Autocurricula
Boling Yang, Liyuan Zheng, Lillian J. Ratliff +2
Autocurricular training is an important sub-area of multi-agent reinforcement learning~(MARL) that allows multiple agents to learn emergent skills in an unsupervised co-evolving sc…
Continuous Versatile Jumping Using Learned Action Residuals
Yuxiang Yang, Xiangyun Meng, Wenhao Yu +3
Jumping is essential for legged robots to traverse through difficult terrains. In this work, we propose a hierarchical framework that combines optimal control and reinforcement lea…
Learning to Read Braille: Bridging the Tactile Reality Gap with Diffusion Models
Carolina Higuera, Byron Boots, Mustafa Mukadam
Simulating vision-based tactile sensors enables learning models for contact-rich tasks when collecting real world data at scale can be prohibitive. However, modeling the optical re…
MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations
Anqi Li, Byron Boots, Ching-An Cheng
We study a new paradigm for sequential decision making, called offline policy learning from observations (PLfO). Offline PLfO aims to learn policies using datasets with substandard…