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cs.RO2026

HumanTracker: Towards Comprehensive and Human-Aligned Motion Tracking Benchmark

Dairu Liu, Zekun Qi, Jiayu Zeng +11

Humanoid motion tracking is central to teleoperation and whole-body imitation, yet evaluation often disagrees with what people perceive in videos. Kinematic errors average per-fram…

cs.RO2026

MM-Nav: Multi-View VLA Model for Robust Visual Navigation via Multi-Expert Learning

Tianyu Xu, Jiawei Chen, Jiazhao Zhang +5

Visual navigation policy is widely regarded as a promising direction, as it mimics humans by using egocentric visual observations for navigation. However, optical information of vi…

cs.RO2026

DeformGen: Dynamics-Based Topology Augmentation for Deformable Manipulation Policy Learning

Zili Lin, Wenyao Zhang, Yuyang Zhang +9

Demonstration augmentation is proposed for cost-efficient data acquisition, but existing methods are fundamentally limited in deformable manipulation due to two challenges: (1) the…

cs.RO2026

LIMMT: Less is More for Motion Tracking

Yu Guan, Zekun Qi, Chenghuai Lin +7

We argue that high-quality motion data can steer tracking policies toward better optimization trajectories early in training. In this work, we introduce LIMMT (Less Is More for Mot…

cs.RO2026

Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking

Zekun Qi, Xuchuan Chen, Dairu Liu +10

We introduce Humanoid-GPT, a GPT-style Transformer with causal attention trained on a billion-scale motion corpus for whole-body control. Unlike prior shallow MLP trackers constrai…

cs.RO2026

Switch-JustDance: Benchmarking Whole Body Motion Tracking Controllers Using a Commercial Console Game

Jeonghwan Kim, Wontaek Kim, Yidan Lu +9

Recent advances in whole-body robot control have enabled humanoid and legged robots to perform increasingly agile and coordinated motions. However, standardized benchmarks for eval…