3 papers
cs.RO2026
Humanoid-DART: Humanoid Loco-Manipulation using Diffusion-guided Augmentation through Relabeling and Tracking
Pranav Debbad, Kanish Thiagarajan, Victor Dhédin +2
Imitating human demonstrations has emerged as a dominant paradigm for learning humanoid loco-manipulation policies. However, scaling these approaches remains challenging due to the…
cs.RO2026
SLowRL: Safe Low-Rank Adaptation Reinforcement Learning for Locomotion
Elham Daneshmand, Shafeef Omar, Glen Berseth +2
Sim-to-real transfer of locomotion policies often leads to performance degradation due to the inevitable sim-to-real gap. Naively fine-tuning these policies directly on hardware is…
cs.RO2025
Learning to Act Through Contact: A Unified View of Multi-Task Robot Learning
Shafeef Omar, Majid Khadiv
We present a unified framework for multi-task locomotion and manipulation policy learning grounded in a contact-explicit representation. Instead of designing different policies for…