cs.RO · 2026-07-20 · No. 59

Robotics, 2026-07-20.

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

01 — The papers

7 entries
  1. 01

    Learning Reach-Avoid Task with Reinforcement Learning: Vectorized Simulation and Benchmark

    Jonas Weihing, Shahram Eivazi

    cs.RO · cs.LG

    Deep reinforcement learning (DRL) has a longstanding tradition in addressing the reach-avoid task problem, especially for controlling robotic arms. While this task serves as a baseline environment within the research community, the ability of DRL to effectively learn the each-avoid task in complex and realistic scenarios beyond simplified and restricted tabletop settings remains uncertain. In this paper, we present, for the first time, a...

    arxiv.org/abs/2607.15935 · PDF

  2. 02

    Dynamics-Aware Meta-Imitation for Generalization to Unseen Robotic Manipulation

    Zhenduo Shang, Xiyao Liu, Bohan Li, Xudong Wang, Teng Ren, Lianqing Liu, Zhi Han

    cs.RO · cs.LG

    Imitation Learning aims to learn skills from extensive observations and demonstrations for robots, so it suffers from data scarcity and environment generalization. The existing methods predominantly focus on imitation from in-domain tasks and consequently struggle with generalization to unseen tasks. To bridge this generalization gap, we propose the \textbf{D}ynamics-\textbf{A}ware \textbf{M}eta-\textbf{I}mitation (DAMI) framework. By...

    arxiv.org/abs/2607.15880 · PDF

  3. 03

    IMBench: A Benchmark for Intuitive Robotic Manipulation

    Anurag Maurya, Sukhvansh Jain, Prajwal Avhad, Gautham Balachandran, Ziyi Zhou, Atharva Kshirsagar, Satyam Singh,...

    cs.RO · cs.AI

    Humans combine reasoning and motor control to solve complex manipulation tasks under diverse constraints. They build an understanding of the physical world that helps them convert reasoning into actions and quickly adapt to new scenes, tasks, and rules. We refer to this capability as intuitive manipulation. Existing benchmarks fail to capture this integration: they evaluate physical reasoning in isolation from execution, or measure policy...

    arxiv.org/abs/2607.15641 · PDF

  4. 04

    Think at 5 Hz, Act at 20 Hz: Asynchronous Fast-Slow Vision-Language-Action Inference for Closed-Loop Driving

    Yun Li, Jiachen Gong, Simon Thompson, Ehsan Javanmardi, Qunli Zhang, Zifan Zeng, Shiming Liu, Peng Wang, Zixuan Guo,...

    cs.RO · cs.AI

    Large language models bring instruction following and scene reasoning to end-to-end driving, but their inference latency collides with the control rate a vehicle requires. Existing closed-loop agents hide this gap by invoking the model on alternate simulation ticks and replaying the previous command in between, so half of all control outputs ignore the newest observations. We present a fast-slow architecture that removes this compromise. A...

    arxiv.org/abs/2607.15621 · PDF

  5. 05

    AEGIS: Assay-Aware Protocol Validation and Runtime Monitoring for Open-Source Liquid Handling Robots

    Priyanka V. Setty, Arvind Ramanathan, Ian Foster, Rick Stevens

    cs.RO · cs.AI

    Self-driving laboratories increasingly rely on low-cost liquid handlers such as the Opentrons OT-2, which ship without the pressure-based aspiration monitoring of Hamilton or Tecan systems and are typically run open-loop. Two failure modes go undetected: protocols that are syntactically valid but violate assay-specific invariants (e.g., tip reuse between a PCR template and a no-template control), and physical execution failures (partial...

    arxiv.org/abs/2607.15620 · PDF

  6. 06

    MemoGuard: An Adaptive Runtime for Guarding Against Memory Traps in Communication-Limited Robot Navigation

    Rajat Bhattacharjya, Hyeonjong Ju, Sing-Yao Wu, Eli Bozorgzadeh, Nikil Dutt

    cs.RO · cs.AI · cs.NI · eess.SY

    Communication-limited robots in mission-critical scenarios such as disaster inspection and search-and-rescue must make reliable onboard decisions without access to remote operators or high-capacity reasoning services. Episodic memory reuse is an attractive low-cost fallback, but retrieval similarity does not guarantee execution validity, i.e., a retrieved action may match the current context yet be unsafe due to changed topology, insufficient...

    arxiv.org/abs/2607.15589 · PDF

  7. 07

    RoboTTT: Context Scaling for Robot Policies

    Yunfan Jiang, Yevgen Chebotar, Ruijie Zheng, Fengyuan Hu, Yunhao Ge, Jimmy Wu, Tianyuan Dai, Scott Reed, Li Fei-Fei,...

    cs.RO · cs.AI · cs.LG

    Recent robot foundation models operate with single-step or short-history visuomotor context. We introduce Test-Time-Training Robot Policies (RoboTTT), a robot model and training recipe that scale visuomotor context to 8K timesteps, three orders of magnitude beyond state-of-the-art policies, without growing inference latency. At this context length, we unlock new robot capabilities: one-shot in-context imitation from human video...

    arxiv.org/abs/2607.15275 · PDF

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