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Only Research#Safety and reliability
Nov 9Mon
  1. Tech Xplore Robotics38

    DTU joint project uses PVD coatings and metasurfaces to alter drone radar signatures

    DTU is collaborating with Aarhus University, the Danish Technological Institute, and companies including TICRA and Arctic Unmanned to use physical vapor deposition (PVD) to produce ultrathin coatings that enhance or reduce drone radar reflection signals. Initial tests on simple shapes such as rectangular plates and cylinders achieved significant attenuation of reflected signals. The next step will be to adapt the method to drones with more complex geometries and test its radar detection effects in real environments. Another route in the project is to develop tunable artificial metasurfaces, which are said to potentially cost less to manufacture than PVD processing.

  2. arXiv cs.RO71

    Onboard Dynamic Obstacle Avoidance System for Small Quadrotor UAVs Based on mmWave Radar

    The authors propose the first mmWave radar-based onboard perception-and-control dynamic obstacle avoidance system for small quadrotor UAVs. The system combines a lightweight interacting multiple models (IMM) tracker with a control barrier function (CBF) controller that directly outputs evasive accelerations, and derives sufficient avoidance conditions relating sensing range, relative speed, and control delay.

  4. arXiv cs.RO80

    Learning-augmented robotic automation achieves cable insertion and soldering on motor production line

    The study proposes Learning-Augmented Robotic Automation, a hybrid system that integrates learned task controllers and a neural 3D safety monitor into conventional industrial workflows to automatically perform flexible cable insertion and soldering on a motor production line. Using less than 20 minutes of real-world data per task, the system ran continuously for 5 hours 10 minutes without physical fencing, producing 108 motors with a 99.4% pass rate on product-level quality inspection, a takt time close to that of human workers with smaller cycle-time variation, and solder-joint quality comparable to that of skilled operators.

    Why it matters: The paper reports data from continuous operation of learning-augmented industrial automation on a real production line for 5 hours 10 minutes, with 108 motors and a 99.4% pass rate, serving as a practical reference for learned control entering manufacturing.

  5. arXiv cs.RO58

    Adaptive collision sensitivity framework for efficient and safe human-robot collaboration

    The authors propose an adaptive collision sensitivity framework that dynamically estimates collision force based on the effective mass (EM) of each robot link and uses it to decide whether to interrupt motion, instead of applying a fixed contact threshold to the whole robot. Experiments were conducted on a real UR10e robot with AIRSKIN sensitive skin and in simulation, supplemented by simulated experiments on a KUKA LBR iiwa using joint torque sensing. In a mock pick-and-place scenario including both transient and quasi-static collisions, production efficiency improved by more than 45% compared with the standard method.

  8. arXiv cs.RO69

    TPS-A*: Path Planning for Any-Shape Vehicles with Certified Collision Checking in Trajectory Parameter Space

    The authors propose TPS-A*, a lattice planner whose edges are kinematically feasible trajectories produced by parameterized trajectory generators, with certified collision checking performed in trajectory parameter space. A grid precomputed per footprint needs only a single pass over local obstacles to return a certified free distance for every candidate trajectory, making the per-node collision cost independent of footprint complexity and trajectory count.

  9. arXiv cs.RO58

    Range-only HOCBF-QP framework enables safe circumnavigation by heterogeneous-orbit multi-robot systems

    The paper proposes a safety-critical control framework for nonholonomic multi-robot systems that lets each robot circumnavigate a stationary target at a different orbital radius while maintaining spacing from neighbors and avoiding collisions. Each robot measures only its distance to the target and exchanges information only with its two adjacent robots; it drives the radius to the target value via acceleration, balances the formation via turn-rate, and uses a higher-order control barrier function quadratic program (HOCBF-QP) to simultaneously constrain target distance, geofence, and inter-robot safety.

  10. arXiv cs.RO65

    HomeLoop: A Robot-IoT Collaborative Simulation Framework and Benchmark for Home Assistance and Safety

    The authors propose HomeLoop, a simulation framework and benchmark built on Isaac Sim that integrates robot bodies, networked devices and wearables, simulated residents, faulty sensors, and hazards that evolve over time within a single environment. The benchmark contains 20 tasks across 5 categories, where the same alert can have different causes. A runtime monitor uses temporal logic to score both the final outcome and process actions, and each action has a preset cost.

  11. arXiv cs.RO69

    CS-Pi: Constraint-Satisfying Flow Policy Sampling at Inference Time

    CS-Pi proposes a "sample-project-renoise" flow-matching policy sampler that satisfies constraints absent from demonstrations at inference time while keeping actions plausible under the policy. The method projects each sample onto the nearest safe action, then adds noise back according to the size of the projection displacement and reruns the original policy until the sample is safe. On a 7-DoF shelf grasping task it achieves an 81% safe success rate, and 55% in zero-shot deployment on Franka, versus 55% and 40% for projection alone. The paper has been accepted to CoRL 2026.

Nov 5Thu
  1. arXiv cs.RO60

    GhostTac: Contactless Manipulation of Robot Tactile Sensors via Electromagnetic Interference

    GhostTac proposes a contactless attack that exploits the nonlinear rectification and limited-bandwidth amplification effects of electromagnetic interference (EMI) to convert carefully crafted EMI signals into a persistent DC offset, bypassing on-board filtering and causing stable measurement deviations. The authors verified the attack's consistency across 15 tactile sensors of different types on 10 sensor modules and 2 dexterous hands, and demonstrated real-world impact through three case studies: tactile grasping, slip detection, and material classification. For example, it could cause a domestic robot to exert excessive force. The work has been accepted to ACM CCS 2026.

  2. arXiv cs.RO62

    PS2-RL: A Provably Safe and Scalable Reinforcement Learning Framework

    The authors propose PS2-RL, a two-phase framework that uses a learned backup policy to forward-integrate the system dynamics and generate an implicit control-invariant set online, thereby avoiding the dimensionality-scaling problems caused by explicitly constructing control-invariant sets. In the first phase, the backup policy is trained with a safe-arrival value function to achieve safe recovery to a certified base set; in the second phase, an RL policy is trained end-to-end through a differentiable projection layer, strictly guaranteeing the safety induced by the backup policy. The framework does not restrict the underlying RL algorithm and can be integrated into existing training pipelines. It was evaluated on robot control tasks with state dimensions up to 10.

  3. arXiv cs.RO31

    Persona-based MLLM evaluation: how robot type and time of day affect perceived safety on urban streets

    The study used 18 images of Seoul streets (split across daytime/nighttime, road class, and three conditions: no robot, delivery robot, or humanoid robot), having two multimodal large language models, Qwen3.8-27B and Gemma4-26B, each conditioned on 500 synthetic personas, choose the safer image in each pair, and compared the results with 25 human respondents.

  5. arXiv cs.RO58

    Study: Effects of haptic feedback on operator cognition and behavior in ROV teleoperation

    A study accepted to IEEE Telepresence 2026 had ROVs face controlled cross-currents in in-water experiments, conveying environmental information to operators through a soft vibrating haptic touchpad. Results showed that encountering an unforeseen cross-current significantly increased operators' physical and temporal demand, and haptic feedback mitigated these burdens and improved self-assessed performance, but also brought the highest mental demand and frustration; nevertheless, operators drove the ROV significantly faster under the haptic condition than under the current-information-only condition.

  7. arXiv cs.RO39

    OUCH: Objective, Uniform, Controlled Haptic Stimulation Wrist-Worn Device for Automated Neurological Assessment

    OUCH is a wrist-worn pneumatic haptic stimulation device that uses stacked pneumatic pouch actuators, a rigid indenter, and a compliant spacer designed to buckle at a set load, so that the indenter contacts the skin only during actuation. At 200 kPa pouch inflation pressure, its indenter's contact pressure is estimated to reach 18,500 kPa, more than 25 times that of a commercially available handheld pressure algometer tip, while requiring less total force. In preliminary perceptual testing, the force required for this indenter to elicit discomfort was about 89% lower than that of the pressure algometer, providing a basis for controllable, repeatable noxious stimulation at the wrist.

  8. NASA58

    NASA SPLICE navigation experiment completes simulated lunar landing test flight

    At Rogers Dry Lake in Edwards, California, NASA completed simulated lunar descent and landing maneuvers using an Alta-X drone carrying the SPLICE guidance, navigation, and control payload. The SPLICE payload uses its guidance system to map terrain and assess landing zones, redirecting to a safer location when it determines the target area is unsafe, and generates high-resolution digital elevation maps from light detection and sensor data.

Nov 2Mon
  1. arXiv cs.RO52

    Detect and Suppress: A Mechanistic Defense Against Adversarial Patches on VLA Models

    The authors use a sparse autoencoder (SAE) to analyze the internal representations of a VLA model, identify a feature whose activation strongly correlates with the presence of adversarial patches, and suppress that feature at inference time only when a linear probe detects an attack, thereby improving robustness without fine-tuning the VLA. In adversarial patch attack tests on LIBERO-10, conditional intervention improved the success rate under intermittent attacks, whereas continuously applying the same intervention substantially degraded policy performance.

  2. arXiv cs.RO62

    AlphaAdj: A Robot Navigation Framework That Adaptively Tunes Control Barrier Functions with Vision-Language Models

    The authors propose AlphaAdj, a vision-to-control navigation framework that uses first-person RGB observations and a vision-language model (VLM) to adjust the conservativeness of control barrier functions (CBFs) according to visual semantic context. The VLM outputs a scalar contextual caution signal that dynamically modulates the CBF parameter alpha, while a geometry- and speed-dependent cap bounds how aggressive the behavior can be. To cope with VLM latency, inference and the control loop run asynchronously, and the semantic signal is taken directly from token-level output probabilities rather than from full autoregressive text generation.

  3. arXiv cs.RO56

    Specification-aware action distribution optimization framework for robotics foundation models

    The paper proposes a specification-aware action distribution optimization framework that enforces a class of Signal Temporal Logic (STL) constraints at execution time without modifying the parameters of a pretrained robotics foundation model. At each decision step, the method reasons over the remaining horizon via forward dynamics propagation to compute a minimally modified action distribution that satisfies a hard STL feasibility constraint, and it is validated in simulation with state-of-the-art robotics foundation models across multiple environments and complex specifications.

  4. arXiv cs.RO72

    CHOP: Counterfactual human preference labels improve obstacle avoidance in visuomotor navigation policies

    The authors propose CHOP, which fine-tunes visuomotor navigation policies with counterfactual human preference labels to align them with human intuition about safety and obstacle avoidance. On the SCAND dataset, multiple advanced models fine-tuned with CHOP reduced near-collision events by an average of 49.7%, decreased deviation from human-preferred trajectories by 45.0%, and increased average obstacle clearance by 19.8%.

  6. arXiv cs.RO34

    Robot Navigation and Canine Welfare: A Pilot Study Surfacing Challenges for Cross-Species Legibility and Proxy Validity

    A pilot study in a real home environment evaluated a "curve-out" navigation maneuver for canine welfare: a lateral arcing pass-by trajectory triggered at the dog's alert distance. Contrary to the hypothesis, the maneuver elicited a stronger stress response than a straight-line baseline. The authors argue that the maneuver's discontinuous execution, with the robot stopping and reorienting multiple times to approximate the arc, undermined behavioral legibility, which itself became a stressor. The study thus raises the legibility of robot behavior to non-humans and proxy stimulus validity as structural challenges for animal-machine interaction (AMI).

  7. arXiv cs.RO40

    How a team that cannot communicate learns its own state from an opponent's reactions

    A game theory and multi-agent study proposes that a team unable to communicate can learn information no single member can see by reading the opponent's reaction to the group as a whole (a tell); one tell can substitute for messaging among all members. In a solved Gaussian model, the team obtains the truth by committing not to use all of the information, and retains the hidden cost the opponent would have paid; an exact test can determine when this conclusion holds in finite games. The study also analyzes how trust is allocated, noting that two interchangeable members responding in different ways can substitute for a single member's private random source.

  8. arXiv cs.RO71

    ROOF: A Learning Method for Humanoid Fall Recovery in Overhead-Constrained Environments

    ROOF proposes a reinforcement learning method with contact-supported geometric escape guidance that enables proprioception-only humanoid robots to first create clearance and then complete the rise under overhead obstacles. In simulation, the recovery success rate was 88.5% under plates that can only be lifted, compared with 18.9% without geometric guidance; it was 98.6% under free plates with randomized size, mass, and placement. After deployment on a Unitree G1, the policy successfully recovered in 95 of 150 attempts across seven real-world obstacle scenes, including furniture and multi-object arrangements not seen in training.

  9. arXiv cs.RO62

    VLA policies still start executing without instructions: research on unintended task execution and start suppression

    The study applied five types of instructions—empty, neutral, meaningless, infeasible, and wrong-task—to six model checkpoints in the LIBERO-Goal simulation, holding the initial state fixed and varying only the instruction, to examine whether the VLA policy starts, which task it completes, and what the instruction determines.

  10. arXiv cs.RO58

    AC-SMPC: Differentiable Actor-Critic MPC with State Constraints for Safe and Fast Robot Navigation

    The authors propose AC-SMPC, an actor-critic model predictive control framework with state constraints for safe navigation in constrained dynamic environments. The method represents the robot and obstacles as vertices and the workspace as half-spaces, locally linearizes the nonlinear dynamics, and convexifies hyperplane-based collision avoidance constraints into a differentiable MPC layer; the actor adjusts MPC objective parameters via closed-loop feedback, while the critic evaluates long-term task performance.

  11. arXiv cs.RO66

    SAFARI: Semantic- and Frontier-Driven Autonomous Risk Inspection Framework

    SAFARI treats object detection as a native channel of a unified probabilistic map, letting geometric and semantic evidence jointly drive exploration and planning. The authors completed 11 missions on a quadruped platform across three field sites, achieving an average coverage of 98.7% of the accessible mission area, with 97.6% of mission time requiring no operator intervention, and detecting objects with characteristic dimensions as small as 15 cm. Ablation experiments show that object-size awareness and safety-aware path planning are critical for hazardous-object search.

  12. arXiv cs.RO60

    Joint-Health-Aware Reinforcement Learning Framework for Robust Control Under Actuator Degradation

    The study proposes a joint-health-aware reinforcement learning framework that encodes actuator condition in a continuous joint-health vector and uses HealthNet to estimate each joint's health from recent observations and actions, allowing the policy to redistribute control effort across healthy and degraded joints.

  13. arXiv cs.RO59

    TRACE: Target-State Risk-Gated Execution for Embodied Visual Tracking

    The research team proposes TRACE, an execution-time target-state risk gate that intervenes selectively while keeping the learned policy unchanged. They first used PPO to update only the planning head of a 0.6B VLA tracker; success and following rates improved across all three EVT-Bench settings, but the collision rate rose in distracted tracking scenarios.

  14. arXiv cs.RO66

    CC-HGCBF: Zero-Shot Safe Navigation for Heterogeneous Mobile Robot Teams

    The authors propose the Capability-Conditioned Heterogeneous Graph Control Barrier Function (CC-HGCBF), which provides a unified safety representation for robots with differing kinematic and actuation capabilities in a shared second-order reference-point operational space. The method introduces the Radial Capability Field (RCF), which characterizes each robot's state-dependent finite-horizon reachable velocity set in a fixed dimension, enabling the shared graph barrier and decentralized policy to adapt to changes in state, kinematic constraints, and control limits without predefined robot identities.

  15. arXiv cs.RO60

    CA-BRF: A Capability-Aware Backup-Recoverability Filter for Heterogeneous Multi-Robot Navigation

    The authors propose a Capability-Aware Backup-Recoverability Filter (CA-BRF) that constructs a backup-recoverable set in a shared Cartesian operational space and uses it to screen candidate controls, so that each robot retains a certified recovery maneuver consistent with its own kinematic and actuation constraints. The method selects admissible interpolations between candidate and backup actions and coordinates simultaneous interactions, thereby guaranteeing recursive feasibility and safety of the discrete-time closed-loop system even when the nominal safety-constrained optimization is infeasible. Simulations cover up to 30 heterogeneous robots, and experiments involve five distinct kinematic classes.

  16. arXiv cs.RO60

    Veloxity: Memory-Safe UAV Embedded Firmware Written in Rust

    The authors present Veloxity, an open-source embedded UAV autopilot written entirely in the memory-safe Rust language, offering a minimal, modular foundation for autonomous drones. They compared it with equivalent C++ autopilots in simulation, on hardware testbenches, and in outdoor waypoint-tracking flight tests; the results showed functional parity with improved performance, indicating that memory-safe languages can be used for reliable, high-performance autonomous aerospace firmware.

  17. arXiv cs.RO55

    Confidence-Modulated Safety Filtering for End-to-End Policies

    The authors propose a filter-agnostic pipeline that modulates the conservativeness of a safety filter at runtime using policy confidence: inside the filter's operational design domain (ODD), confidence is traded for performance; beyond the ODD, low confidence yields additional safety margin. Preliminary F1TENTH simulations show that the modulated filter combines the strengths of permissive and conservative filters.

Oct 29Thu
  1. Guyuehome58

    FTNCBF: Fault-Tolerant Neural Control Barrier Functions Under Sensor Failures and Attacks

    FTNCBF addresses the failure of barrier-function safety guarantees when sensors are attacked. It runs estimators in parallel for each fault hypothesis, converts estimation uncertainty into first- and second-order correction terms in the barrier condition, and then synthesizes fault-tolerant control via a minimum-norm program. The method was reproduced in spacecraft and UAV scenarios.

Oct 28Wed
  1. Robotics & Automation News55

    Leiden University study: Ryberg Healthcare's autonomous UV-C disinfection robot Omnia meets disinfection standards

    Researchers at Leiden University evaluated Ryberg Healthcare's autonomous UV-C disinfection robot Omnia in a laboratory designed to emulate a hospital environment; the results, published in the peer-reviewed journal Infection Prevention in Practice, show it met the acceptance criterion of at least 5 log reduction.

  2. 爱范儿58

    Vine-inspired soft robotic intubation system SRIS achieves 87% first-attempt success rate in cadaver tests

    A team led by Elliot Hawkes at Stanford University designed the soft robotic intubation system SRIS based on the vine robot principle. In cadaver tests, 12 participants, including 8 emergency responders, received 5 minutes of training; 87% achieved successful intubation on the first attempt, with an average time of 21 seconds. Under the same conditions, video laryngoscopy had a 63% success rate and an average time of 44 seconds.