cs.RO · 2026-07-13 · No. 52

Robotics, 2026-07-13.

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

01 — The papers

5 entries
  1. 01

    PAC-ACT: Post-training Actor-Critic for Action Chunking Transformers

    Yujie Pang, Zudong Li

    cs.RO · cs.AI

    Precision industrial contact manipulation requires reliable robot policies under pose perturbations and contact-force constraints. Vision-language-action models offer broad generalization but often introduce high inference latency and GPU-memory cost, while vision-action chunking policies are more suitable for real-time industrial control. However, these policies are usually trained by behavior cloning and suffer from distribution shift in...

    arxiv.org/abs/2607.09590 · PDF

  2. 02

    Tactile and Vision Conditioned Contact-Centric Control for Whole-Arm Manipulation

    Rishabh Madan, Angchen Xie, Samantha Saak, Andres Blanco, Dohyeok Lee, Sarah Grace Brown, Yunting Yan, Mark Zolotas,...

    cs.RO · cs.AI

    Whole-arm manipulation involves direct contact with the environment while the robot completes a task by distributing contact across multiple links as contacts form, slide, and break. This setting breaks common implicit assumptions in many learning-based manipulation pipelines: arm configuration tightly couples motion and contact forces, contact state is partially observed under occlusion, and purely learned rollouts can become physically...

    arxiv.org/abs/2607.09218 · PDF

  3. 03

    GenVid2Robot: From Video Generation to Robot Manipulation via Rigid-Geometric Consistency

    Haohui Huang, Xi Yuan, Panpan Liao, Tao Teng, Chenguang Yang, Jing Guo, Yi Guo

    cs.RO · cs.LG

    Generated videos provide useful visual motion priors for robot manipulation, but their visual plausibility does not imply physical executability. A generated video usually lacks metric geometry, grasp grounding, robot kinematic feasibility, and execution-time feedback, which makes direct trajectory replay unreliable in real-world manipulation. This paper presents GenVid2Robot, a rigid-geometric consistency framework that converts generated...

    arxiv.org/abs/2607.09191 · PDF

  4. 04

    CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding

    Yuri Ishitoya, Jeremy Siburian, Masashi Hamaya, Kuniaki Saito, Cristian C. Beltran-Hernandez, Mai Nishimura

    cs.RO · cs.AI

    Vision-language-action models (VLAs) inherit semantic capabilities from pretrained VLMs, yet large-scale post-training on robot data and architectural modifications can reshape the backbone so extensively that it becomes difficult to isolate what the VLM contributes to control. Directly converting pretrained VLMs into VLAs with minimal architectural change offers a more transparent path to understanding how VLM capabilities transfer across...

    arxiv.org/abs/2607.08974 · PDF

  5. 05

    FlowDAgger: Human-in-the-Loop Adaptation of Generative Robot Policies in Latent Space

    Michael Murray, Daphne Chen, Simran Bagaria, Dean Fortier, Tess Hellebrekers, Galen Mullins, Harshavardhan Gajarla,...

    cs.RO · cs.LG

    Pretrained generative robot policies based on flow matching and diffusion have achieved impressive results across a wide range of manipulation tasks. Yet real-world deployments routinely expose failure modes outside the pretraining distribution. Closing these gaps typically requires large-scale data collection or online reinforcement learning on physical hardware, which is impractical for rapid and safe adaptation. We present FlowDAgger, a...

    arxiv.org/abs/2607.08877 · PDF

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