cs.RO · 2026-09-03 · No. 104

Robotics, 2026-09-03.

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

01 — The papers

6 entries
  1. 01

    Towards Trustworthy Autonomous Robots: An Explainable AI-Based Decision Framework

    Cagri Temel

    cs.RO · cs.AI

    Autonomous robots powered by deep learning face a fundamental auditability challenge: when incidents occur, investigators cannot reconstruct why the system made specific decisions. This paper presents TRACE (Transparent Reasoning Architecture for Credible Execution), a decision framework that ensures every autonomous action can be traced back to sensor evidence through documented causal chains. The framework organizes decision-making into...

    arxiv.org/abs/2609.02861 · PDF

  2. 02

    Humanoid Safe Stop via Learned Stoppability Value

    Junfeng Long, Pieter Abbeel, Koushil Sreenath, Roberto Horowitz, Guanya Shi, C. Karen Liu

    cs.RO · cs.LG · eess.SY

    Humanoid robots responding to emergency stop commands typically execute a fixed maneuver, without reasoning about whether a safe stop is actually feasible from the current state. We cast emergency stopping as a reach-avoid problem and propose Safe-Stop, a task-agnostic framework that pairs a learned stop policy with learned stoppability estimators. The estimators are complementary: a stop-probability estimator supervised by the actual...

    arxiv.org/abs/2609.02358 · PDF

  3. 03

    CrashDiffuser: VLM-Guided Collision Intent Reasoning for Fine-Grained Safety-Critical Traffic Scenario Generation

    Shucheng Zhang, Yuang Zhang, Bingzhang Wang, Muhammad Monjurul Karim, Kehua Chen, Yinhai Wang

    cs.RO · cs.AI

    Generating safety-critical scenarios is essential for evaluating autonomous driving systems. However, existing generators primarily focus on inducing collisions and offer limited control over where contact occurs on the target vehicle. In this paper, we study fine-grained safety-critical scenario generation, where success requires both a target collision and a specified head, rear, or side contact region. We propose CrashDiffuser, a...

    arxiv.org/abs/2609.02270 · PDF

  4. 04

    DiffuSearch: How Hybrid Trajectory Planning Benefits from Aligned Objectives in Diffusion and Action Space

    Steffen Hagedorn, Aron Distelzweig, Alexandru P. Condurache

    cs.RO · cs.AI

    In trajectory planning for autonomous driving, hybrid planning architectures are often realized as a collection of disparate modules, each with its own objectives. This lack of a unifying principle can lead to inconsistencies between the initial and refined trajectory, resulting in suboptimal behavior. We address this by introducing DiffuSearch, a novel hybrid planner that uses a unified set of objectives across generation and refinement. Our...

    arxiv.org/abs/2609.02252 · PDF

  5. 05

    Hardware-Accelerated Instance Segmentation for Resource-Constrained Space Robotics with Criticality Analysis

    Siddhant Shete, Hilmi Dogu Kücüker, Udo Frese, Frank Kirchner

    cs.RO · cs.AR · cs.CV · cs.LG

    Autonomous lunar missions require real-time per- ception under three coupled constraints: extreme low-light conditions, limited onboard compute, and radiation-induced hardware faults that can silently corrupt inference. We present a deployment-oriented instance segmentation framework for resource-constrained lunar robotics that jointly addresses quan- tization calibration and system-level fault exposure under strict compute constraints....

    arxiv.org/abs/2609.02219 · PDF

  6. 06

    Modeling What Changes: Sparse, Residual World Models for Object-Centric Manipulation

    Param Thakkar, Parsika Paresh Shah, Manisha Sushant Gote

    cs.RO · cs.AI

    Monolithic world models predict the entire next state at every step, spending capacity re-predicting the static majority of a scene and injecting error into it. We ask whether explicitly modeling change (a per-object change gate plus a residual delta head that perturbs only the objects the gate flags) is a more effective and interpretable bias for physical prediction and control. On a MuJoCo tabletop pushing benchmark scaling from 3 to 8...

    arxiv.org/abs/2609.02046 · PDF

This edition is part of The Daily Abstract — cs.RO archive. Subscribe to receive these in your inbox each morning, automatically translated to Spanish, with reply-to-PDF: arxivdaily.ignorelist.com.

Colophon Set in Georgia, with system sans for interface chrome and a monospaced stack for code and paper identifiers. Sole accent: amber #D99C5E. Built and served on an always-free VM. The masthead is set 14% letterspaced because newspapers do that and it works.