cs.RO · 2026-06-15 · No. 24
Robotics, 2026-06-15.
12 new papers in cs.RO. Titles, authors,
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01 — The papers
12 entries-
01
Sensitivity Shaping for Latent Modeling
Hongzhan Yu, Chenghao Li, Ruipeng Zhang, Henrik Christensen, Sicun Gao
cs.RO · cs.AI
Generative dynamics models enable planning in challenging robotic systems, but safe deployment requires reliably detecting policy-induced out-of-distribution (OOD) transitions. Existing methods typically treat the learned dynamics as fixed and attach post hoc support surrogates. We show that these surrogates can fail when the dynamics are locally insensitive to critical action choices: unsupported control actions may produce latent...
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02
ORCA: A Platform for Open-Source Dexterity Research
Francesco Capuano, Maximilian Eberlein, Fabrice Bourquin, Clemens Claudio Christoph
cs.RO · cs.LG
Robotics manipulation research increasingly focuses on two-finger parallel grippers for their effectiveness, affordability, and ease of teleoperation. Grippers are nonetheless limited by their form factor, often requiring bimanual setups even for simple reorientation tasks. Anthropomorphic hands are a more natural platform for dexterous robot learning -- closer to the human hand, and capable of learning from human video -- yet they remain...
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03
TRACE: Trajectory-Routed Causal Memory for Delayed-Evidence Visuomotor Imitation
Zihao Li, Ranpeng Qiu, Yincong Chen, Guoqiang Ren, Weiming Zhi
cs.RO · cs.AI
Robots under autonomous operation may require decisions based on evidence that is no longer visible. We study \emph{delayed-evidence} tasks, where an early cue disappears before a later decision point, so visually similar observations can require different actions. In these settings, the current observation is not a sufficient state for control. We introduce TRAjectory-routed Causal Evidence (TRACE), a memory framework for visuomotor...
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04
CADET: Physics-Grounded Causal Auditing and Training-Free Deconfounding of End-to-End Driving Planners
Zikun Guo
cs.RO · cs.AI
End-to-end (E2E) autonomous-driving planners trained by imitation are prone to statistical shortcuts: they associate scene elements that merely co-occur with expert actions (a roadside object, a building facade) with driving decisions, rather than the variables that causally determine them. Such causal confusion silently compromises reliability in long-tail scenarios, and it is difficult to detect, because prevailing open-loop metrics (L2...
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05
Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack
He Zhang, Lingzhu Xiang, Haitao Lin, Zeyu Huang, Minghui Wang, Dingyan Zhong, Yubo Dong, Yihao Wu, Yongming Rao,...
cs.RO · cs.AI
In this report, we present Hy-Embodied-0.5-VLA, abbreviated as HyVLA-0.5, an end-to-end system that spans the full robot learning stack: data collection, model design, continued pre-training and supervised fine-tuning, RL post-training, and real-world deployment. Each component serves a distinct role in this stack.
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06
Elastic Queries Reinforcement Learning: Self-Aware Policy Execution for VLA Models
Ge Wang, Xinyu Tan, Xiang Li, Man Luo, Chengsi Yao, Shenhao Yan, Jiahao Yang, Fan Feng, Honghao Cai, Xiangyuan Wang,...
cs.RO · cs.AI
Vision-language-action (VLA) models are powerful action generators for robot manipulation, but they are typically executed with fixed inference and replanning schedules. This rigidity ignores the uneven difficulty of robot control: contact-rich or uncertain states may need more computation and fresher feedback, while easier states can often be handled with fewer inference steps and longer open-loop execution. We propose Elastic Queries...
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07
Robust Fall Recovery for Armless Bipedal-Wheeled Robots Via Force-Guided Learning
Haidong Hou, Zhangguo Yu, Tao Han, Hengbo Qi, Khaleel Ghazal, Yu Zhang, Yidong Du, Xuechao Chen, Fei Meng
cs.RO · cs.AI
Fall recovery is critical for autonomous legged locomotion. Existing methods have demonstrated that some legged robots, such as humanoids and quadrupeds, are capable of fall recovery from diverse postures by utilizing arms or coordinating multi-legs to generate support forces. Without arms or other legs to provide supportive assistance, a bipedal-wheeled robot must rely solely on the actuation of its legs, making recovery particularly...
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08
When and How Severely: Scenario-Specific Safety Envelopes for Driving VLAs
Abhinaw Priyadershi, Jelena Frtunikj
cs.RO · cs.AI
Safety certification of Vision-Language-Action (VLA) driving planners under ISO 21448 (SOTIF) rests on an Operational Design Domain (ODD) specification that answers two complementary questions: when does the planner start to fail, and how severely does it fail once it does? We evaluate Alpamayo R1, a 10B-parameter open-weight driving VLA, on 15,968 (clip, attack) pairs. We find a conservative-aggregate gap: an aggregate safe threshold of...
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09
Selective Agentic Recovery for UAV Autonomy with a Persistent Mission Runtime
Taewoo Park, Kyeonghyun Yoo, Seunghyun Yoo, Hwangnam Kim
cs.RO · cs.AI
Agentic AI can support unmanned aerial vehicle (UAV) autonomy by providing high-level recovery reasoning when local waypoint- or setpoint-based execution encounters blocked passages, repeated no-progress behavior, or mission-level ambiguity. On physical UAVs, however, remote reasoning is most useful when it is invoked selectively, since each call introduces latency, resource cost, backend uncertainty, and a need to validate the returned...
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10
Universal Manipulation Exoskeleton: Learning Compliant Whole-body Policies with Real-time Torque Feedback
Litian Liang, Jingxi Xu, Xinda Qi, Yujun Cai, Houzhu Ding, Luqi Wang, Zhixin Sun, Jyh-Herng Chow, Ming Yang, Mark Cutkosky
cs.RO · cs.AI · cs.LG
For robots to work safely in household environments, they need to be compliant and react to torque and force feedback during contact. However, the majority of existing data collection pipelines still lack the ability to capture force and torque data for learning active compliant policies. In this paper, we present Universal Manipulation Exoskeleton (UME), an upper-limb exoskeleton that provides real-time haptic torque feedback while recording...
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11
Robustness without Wrinkles: Parallel Simulation and Robust MPC for Certified Deformable Manipulation
Wei-Chen Li, Jeffrey Fang, Sasanka Polisetti, Yuexi Song, Glen Chou
cs.RO · cs.AI · cs.LG · eess.SY · math.OC
We present CORD-SLS, a real-time control method for safe deformable object manipulation, with a focus on ropes and cloth. At its core is a GPU-parallel differentiable simulator with contact smoothing which enables efficient gradient-based planning through intermittent contact. To robustly satisfy constraints under model and sensing uncertainty, we develop a real-time, GPU-parallel output-feedback robust model predictive control (MPC)...
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12
An Attention-based Model for Robust Forecasting with Missing Modality
Zhitian Zhang, Wenjie Zi, Yunduz Rakhmangulova, Saghar Irandoust, Hossein Hajimirsadeghi, Thibaut Durand
cs.RO · cs.LG
Learning with missing modalities is a fundamental challenge in multimodal robot learning, as real-world robotic systems often operate in environments with incomplete sensor data. Attention-based models are appealing for processing multimodal data because they can handle multiple modalities with a single backbone network. However, most multimodal models assume that all modalities are available during both training and inference, limiting their...
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