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Xinqi Li

4 accepted papers

2026

MILD: Tractable Terrain Modeling for Learning Improved Bipedal Locomotion on Deformable Surfaces

RA-L 2026

Enabling robots to walk on yielding terrain is vital for applications ranging from disaster response to planetary exploration. While bipedal robots hold immense potential, their locomotion on deformable surfaces remains limited as current simulators fail to capture the spatiotemporal heterogeneity o

Cited by 1SourceScholar
2026

MILD: Tractable Terrain Modeling for Learning Improved Bipedal Locomotion on Deformable Surfaces

ICRA 2026poster

Enabling robots to walk on yielding terrain is vital for applications ranging from disaster response to planetary exploration. While bipedal robots hold immense potential, their locomotion on deformable surfaces remains limited as current simulators fail to capture the spatiotemporal heterogeneity o…

Cited by 0SourceScholar
2025

Interactive and Balanced Multimodal Learning via Cross Attention and Gradient Modulation for Compressed Video Action Recognition

ICASSP 2025accepted

Compressed video action recognition is a crucial task in video processing. Compared with traditional methods, it directly processes RGB (I-frames) and motion (motion vectors and residuals) modalities, which effectively alleviates computational burdens. However, this task suffers from dynamic noise a…

Cited by 0SourceScholar
2024

MorAL: Learning Morphologically Adaptive Locomotion Controller for Quadrupedal Robots on Challenging Terrains

RA-L 2024

Due to the rapid development of the quadruped robot industry in the past decade, various commercial quadruped robots have emerged with distinct physical attributes. Different from the previous work in which the designed controller is robot-specific, this article proposes a learning-based control fra

Cited by 37SourceScholar