3 embodied-AI and robotics papers accepted at RA-L 2026, 1 with code.3 篇被 RA-L 2026 接收的具身智能与机器人论文，其中 1 篇附代码。
Feed papers are listed when their arXiv comment says they were accepted (e.g. 'Accepted to CoRL 2026'); papers that do not say so are missing, and the list grows as authors post camera-ready versions. Curated entries carry a hand-checked venue.日常 arXiv 论文仅在其 arXiv 备注写明已被接收（如 “Accepted to CoRL 2026”）时列入；未注明的论文不会出现，列表会随作者上传终稿而增加。精选条目的会议/期刊信息经过人工核对。
The simultaneous surveillance and containment (SSC) problem arises in multi-robot applications such as perimeter security and environmental monitoring, where robots must survey a region of interest and coordinate to enclose targets once they are detected. Surveillance favors spatial dispersion for coverage, whereas containment requires target-centric cohesion for enclosure. This conflict makes heuristic switching unstable, and the instability becomes more pronounced as larger robotic networks introduce more inter-agent and agent-target relations. End-to-end DRL reduces reliance on …
Muzaffar Qureshi, Tochukwu E. Ogri, Trivikram Satharasi, Kyle Volle et al.
Abstract摘要
In traditional Gaussian process (GP) regression, the inputs are typically assumed to be known, and uncertainty enters only through the measurements. When scalar fields are mapped using GP regression with measurements obtained from a mobile sensor, the inputs correspond to the estimated pose of the mobile sensor. The goal in this letter is to understand how a recorded GP, trained on erroneous inputs (e.g., pose estimates from dead-reckoning), can be corrected in a numerically efficient way when better estimates of the inputs become available. In the developed bias-aware GP (Ba-GP) framework, …
@misc{qureshi2026input,
title = {{Input Bias Correction in Gaussian Process Regression}},
author = {Muzaffar Qureshi and Tochukwu E. Ogri and Trivikram Satharasi and Kyle Volle and Rushikesh Kamalapurkar},
year = {2026},
eprint = {2610.31920},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.31920},
}
Real-world reinforcement learning has enabled contact-rich manipulation tasks to be trained directly on physical robots, but unsafe interactions during online learning remain a key barrier to practical deployment. We propose Safe Evidential Real-World Reinforcement Learning (SEVI), a framework for reliability-aware safety intervention during real-world policy learning. SEVI introduces an Evidential Safety Critic that estimates both future constraint-violation risk and epistemic uncertainty in a single forward pass. During online learning, SEVI evaluates each task-policy action before …