cs.RO · 2026-08-15 · No. 85

Robotics, 2026-08-15.

4 new papers in cs.RO. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

4 entries
  1. 01

    HumanTracker: Towards Comprehensive and Human-Aligned Motion Tracking Benchmark

    Dairu Liu, Zekun Qi, Jiayu Zeng, Ruixi Yu, Yu Guan, Yintianrun Zhang, Xuchuan Chen, Sikai Liang, Zekai Li, Chenghuai...

    cs.RO · cs.AI · cs.CV

    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-frame pose differences but miss the physical artifacts that matter most, particularly unstable support and incorrect contacts such as foot skating and mistimed touch-downs. Meanwhile, widely used test suites are small and lack the diversity needed to stress...

    arxiv.org/abs/2608.13555 · PDF

  2. 02

    ContactGuard: Pre-Contact Execution Monitoring with Action-Conditioned Latent World Models

    Gehan Zheng, Matthew Johnson-Roberson, Weiming Zhi

    cs.RO · cs.AI · cs.CV

    Contact-rich manipulation failures are often detected only after the robot has committed to contact. This is especially limiting in wrist-camera setups: close gripper--object views help observe contact, but a poor approach may already push, miss, slip, or disturb the object before conventional detectors react. We introduce \emph{ContactGuard}, a pre-contact execution monitor for chunked visuomotor policies. Given the policy's planned action...

    arxiv.org/abs/2608.13438 · PDF

  3. 03

    Deliberate Practice: Learning Robot Skills under a Budget

    Shivam Vats, Sudarshan Harithas, Mete Tuluhan Akbulut, Arvind Raghunathan, George Konidaris

    cs.RO · cs.AI

    We consider the problem of autonomously learning robot skills under a limited practice budget for sequential tasks. We propose an active skill learning algorithm, \emph{Deliberate Practice (DP)}, that computes a provably \emph{budget-optimal} allocation---practicing skills that maximize expected cumulative reward while being learnable within the budget. DP estimates both the time needed to master skills and the cumulative reward of the task...

    arxiv.org/abs/2608.13415 · PDF

  4. 04

    BrainWAM: Action-Space Coordination of Semantic Priors and Predictive Dynamics for Autonomous Driving

    Bing Zhan, Shuyao Shang, Jiahao Gu, Shuo Lu, Yuan Xu, Zhao Wang, Yida Wang, Xueyang Zhang, Kun Zhan, Lue Fan, Zhaoxiang Zhang

    cs.RO · cs.AI · cs.CV

    Autonomous driving requires planning under both semantic constraints and predictive dynamics. Existing end-to-end driving approaches, however, typically emphasize only one side of this requirement: Vision-Language-Action (VLA) models exploit VLM priors for semantic reasoning, while World Action Models (WAMs) provide future-aware prediction through generative world modeling. This naturally motivates a unified planner that can leverage both...

    arxiv.org/abs/2608.12854 · PDF

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