Robotics and embodied AI

This ACM Computing Surveys paper, titled "A Comparative Survey of Security Risks in AI Systems: From LLMs to AI Agents and Embodied Agents," appears in Volume 58, Issue 15, pages 1-38, November 2026. The source text provided contains only the citation details, so its research question, method and findings cannot be summarized from it.

Adapdoor is an attack framework that injects action-level backdoors into deep reinforcement learning models during training, so that adversaries can manipulate action outputs at deployment. It initializes the backdoor reward from benign reward statistics and iteratively fine-tunes it using performance feedback from benign and backdoor tasks. Across 3 DRL algorithms, 11 environments and 53 backdoor tasks, it outperforms existing baselines by 42.0% to 144.2%.

This ACM Computing Surveys article, published in Volume 58, Issue 15 (pages 1-38) in November 2026, surveys security risks across AI systems, from large language models to AI agents and embodied agents. The source text provided contains only the bibliographic citation and no article content, so no findings or methods can be reported from it.

Researchers propose an RL-based routing scheme for UAVs that uses an LLM to infer environmental and jamming features from multimodal sensing data, along with neighbor location and speed, to choose next hops and relay power. Trust management built from packet forwarding and environmental features selects higher-trust next hops and enables self-healing against gray-hole attacks, while penalizing decisions that trigger frequent path reconstructions. Experiments with a 7-billion-parameter LLaVA model at the control center show performance gains against smart jamming and gray-hole attacks.

The source is a survey titled "Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attacks," published in ACM Computing Surveys, Volume 58, Issue 12, pages 1-36, September 2026. The provided text contains only the publication details and no article content describing its findings or methods.

Researchers propose most overestimated Q value regularization (MQR), an offline reinforcement learning algorithm for high-dimensional discrete action spaces. MQR penalizes the action with the most overestimated Q value rather than applying uniform penalties across the whole action space. On a robotic pushing and grasping task, it reached a 96.94% clearance rate in simulation and 99.04% in real-world dense configurations, and the code is publicly available.