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20242026
most citedFR-Net: Learning Robust Quadrupedal Fall Recovery on Challenging Terrains through Mass-Contact Prediction

9 citations · 9 across the 11 of their papers we have counts for

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Showing 2026 · cs.ROShow all

7 papers · 2 filters

cs.RO2026

HiPHI: A Large-Scale Benchmark for High-Precision Human Motion and Object-Interaction

Jiahao Ji, Ji Ma, Runhan Zhang +12

Humanoid intelligence requires learning over an extremely diverse space of whole-body motions and physically grounded interactions. However, existing embodied datasets remain funda…

cs.RO2026

Unified Motion Retargeting for Humanoids with Learned Point Cloud Correspondence

Hanyang Cao, Yuetong Fang, Taesoo Kwon +10

Humanoid learning increasingly relies on transforming vast and diverse human motion data into high-quality robot reference trajectories. However, retargeting human motion to humano…

cs.RO2026

PFM-HR: Pose Flow Matching for Humanoid Robots

Yukang Gao, Yi Gu, Yangchen Zhou +9

Motion priors improve reinforcement learning for physics-based humanoid tracking, but temporal priors require ordered motion clips, while pose priors provide limited guidance for p…

cs.RO2026

Learning Highly Dynamic Skills Transition for Quadruped Jumping Through Constrained Space

Zeren Luo, Jiahui Zhang, Yimin Han +4

Although legged animals are capable of performing explosive motions while traversing confined spaces, replicating this behavior in quadrupedal robots has been a longstanding challe…

cs.RO2026

OmniContact: Chaining Meta-Skills via Contact Flow for Generalizable Humanoid Loco-Manipulation

Runyi Yu, Xiaoyi Lin, Ji Ma +11

Learning long-horizon humanoid loco-manipulation poses a dual challenge: it requires not only the robust execution of meta-skills but also their seamless, closed-loop chaining equi…

cs.RO2026

Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning

Shihao Dong, Yeke Chen, Zeren Luo +8

While single-agent legged locomotion has witnessed remarkable progress, individual robots remain fundamentally constrained by physical actuation limits. To transcend these boundari…

cs.RO2026

Learning Human-Like Badminton Skills for Humanoid Robots

Yeke Chen, Shihao Dong, Xiaoyu Ji +12

Realizing versatile and human-like performance in high-demand sports like badminton remains a formidable challenge for humanoid robotics. Unlike standard locomotion or static manip…