2 citations · 2 across the 7 of their papers we have counts for
8 papers
Learning Expressive and Compositional Motion Representation via Spectral Skills
Feiyang Wu, Chenxiao Gao, Chen Yang +3
Robotic foundation models offer a promising path toward general-purpose humanoid robot control, often through hierarchical architectures. However, their effectiveness depends on th…
Opt2VLA: Force-Aware Vision-Language-Action for Contact-Rich Humanoid Whole-Body Manipulation
Fukang Liu, Yipu Chen, Jaehwi Jang +3
Humanoid robots are expected to perform diverse human-level tasks in daily environments, many of which require precise regulation of interaction forces. While recent vision-languag…
VQActFlow: Vector-Quantized Action Mode Steering for Multi-Task Robot Manipulation
Zhigen Zhao, Mark Leggiero, Yipu Chen +5
Multi-task robot manipulation policies are challenging to learn from demonstration because traditionally a single network must select among qualitatively different action modes fro…
WT-UMI: Tactile-based Whole-Body Manipulation via Force-Supervised Contact-Aware Planning
Jaehwi Jang, Zhaoyuan Gu, Alfred Cueva +15
Whole-body humanoid manipulation of bulky, deformable, and shared-load objects requires distributed contact sensing and explicit force regulation, yet most imitation policies treat…
PPF: Pre-training and Preservative Fine-tuning of Humanoid Locomotion via Model-Assumption-based Regularization
Hyunyoung Jung, Zhaoyuan Gu, Ye Zhao +2
Humanoid locomotion is a challenging task due to its inherent complexity and high-dimensional dynamics, as well as the need to adapt to diverse and unpredictable environments. In t…
Opt2Skill: Imitating Dynamically-feasible Whole-Body Trajectories for Versatile Humanoid Loco-Manipulation
Fukang Liu, Zhaoyuan Gu, Yilin Cai +8
Humanoid robots are designed to perform diverse loco-manipulation tasks. However, they face challenges due to their high-dimensional and unstable dynamics, as well as the complex c…
Signal Temporal Logic-Guided Model Predictive Control for Robust Bipedal Locomotion Resilient to Runtime External Perturbations
Zhaoyuan Gu, Rongming Guo, William Yates +2
This study investigates formal-method-based trajectory optimization (TO) for bipedal locomotion, focusing on scenarios where the robot encounters external perturbations at unforese…
Infer and Adapt: Bipedal Locomotion Reward Learning from Demonstrations via Inverse Reinforcement Learning
Feiyang Wu, Zhaoyuan Gu, Hanran Wu +2
Enabling bipedal walking robots to learn how to maneuver over highly uneven, dynamically changing terrains is challenging due to the complexity of robot dynamics and interacted env…