The paper proposes the Semantic Autonomy Stack (SAS), a six-layer integration framework that builds semantic reasoning, mission interface, and operational memory layers on a validated ROS 2/Nav2 foundation. It uses a deterministic-first cascade that escalates unresolved or guarded cases to a pretrained vision-language model (VLM).
The authors propose a unified system that simultaneously constrains collision risk along the whole-body trajectory and target visibility during grasping in unknown and dynamic environments, comprising three parts: a velocity-aware gaze policy, a hierarchical subgoal policy, and a receding-horizon whole-body planner. In 400 randomized simulation scenarios and real-world trials on a Fetch mobile manipulator, the system achieved success rates of 70.4% in unknown static environments and 57.9% in dynamic environments, 16.3 and 9.3 percentage points higher than decoupled navigation and manipulation policies, respectively, while significantly reducing collisions.
PrepTurn is a reinforcement learning framework for anticipatory maneuvering in continuous local navigation. It maps the current and next pose goals directly to joint actions, letting the upcoming goal influence how the robot approaches the current one. The framework uses a persistent-credit reward to encourage completing the entire goal sequence while in motion, and an automatic curriculum that gradually increases goal difficulty to stabilize turning behavior; simulation and onboard Unitree Go2 experiments show high sequential traversal completion rates and reduced stop-and-go motion.
UniManip proposes a unified framework centered on the Agentic Operational Graph (AOG) for task orchestration, state tracking, execution verification, and closed-loop failure recovery in open-world robotic manipulation. The method grounds high-level semantic intent into executable primitives through a physically grounded scene graph, and combines a safety-aware local planner to generate collision-free trajectories. It achieves state-of-the-art performance on LIBERO-Pro, and in real-world experiments its task success rate is 22.5% and 25.0% higher than leading VLA models and hierarchical baselines, respectively.
The authors propose dynamic window pure pursuit (DWPP), which moves command velocity computation into the v-ω velocity space and selects, within the dynamic window, the point closest to the line ω=κv, thereby explicitly incorporating velocity and acceleration constraints. Experiments show that DWPP avoids constraint-violating commands and achieves better path-tracking accuracy than conventional pure pursuit methods. The method has been integrated into the official Nav2 repository and made publicly available, and the paper has been accepted by Robotics and Autonomous Systems.
This survey constructs a closed-loop task-execution architecture for collaborative continuum robots (CCRs) from an automation-system perspective, explaining how target-task specifications are transformed into coordinated execution by multiple continuum robots. Based on this, the authors classify CCRs into three modes: separated collaboration, assistance collaboration, and parallel collaboration. They then review research progress in terms of multi-robot task allocation, structural design, modeling, motion planning, control strategies, and sensing mechanisms, and finally discuss key technical opportunities and the system-integration challenges of real-world deployment.
The authors propose IndustryNav, a simulation environment with 24 warehouse scenes for closed-loop navigation to specified destinations, and introduce safety metrics that record collisions and near-collision warnings. Experiments with 11 leading open- and closed-source VLLMs and 3 navigation baselines show that agents can reach their goals yet collide with moving obstacles, exposing a gap between task success and safe navigation.
The paper proposes a shared control framework for mobile robots with planning-level intention prediction, using an intention domain to constrain path replanning and jointly modeling intention-domain prediction and replanning as a Markov decision process solved via deep reinforcement learning. The authors also design a Voronoi-based human trajectory generation algorithm that allows the model to be trained entirely in simulation without human participation or demonstration data; simulations and real-world user studies show the method reduces operator workload and improves safety without degrading task efficiency.
The paper proposes OpTTA, which adds the planner's goal-reaching cost term to the test-time adaptation objective for world models, keeping the model optimistic about reaching the goal. Under visual and environmental shifts in manipulation, reaching, and navigation tasks, when only the predictor is adapted, OpTTA raises the final planning success rate by an average of 13.5 percentage points, compared with 6.9 percentage points for AdaJEPA, which uses only the prediction objective; when the encoder is also adapted, the two achieve 13.5 and 7.5 percentage points, respectively.
This project explores controller design and trajectory generation for a quadrotor. It uses a differential flatness approach to generate minimum-snap trajectories based on waypoint navigation, and a simple PD controller to track the trajectories. Simulation results show that the method can generate smooth trajectories for the quadrotor's position and velocity.
The study proposes treating SysML v2 requirements with assumptions and required constraints as assume/guarantee contracts. After decomposing system requirements over fixed neighbors, the weakest sound requirement for an open component is the contract quotient, which can be expressed in Signal Temporal Logic.
TrySee proposes a modular closed-loop robot task planning pipeline that combines general-purpose VLM reasoning with domain-specific perception and existing robotics components, without requiring VLM fine-tuning or end-to-end demonstrations. The system uses reactive cyclic planning and adds domain-specific modular vision preprocessing to provide online grounding, while a dynamic preprocessing selection mechanism improves scalability.
FISRF proposes an agent-and-mentor architecture that estimates a robot's Zone of Proximal Development (ZPD) in task planning and adaptively selects reflection strategies and mentor guidance, mitigating the accumulation of erroneous experience during early interactions. Experiments in ALFWorld and on real-world robotic platforms show that the method reduces average error-correction rounds by 37% and redundant reasoning by 25.1%, while improving overall task success rate by 8%.
The research proposes a real-time motion planning framework that retains the full SE(2) model of a quadruped robot along with its anisotropic, heading-coupled velocity constraints, and accepts the resulting nonconvexity. The method parameterizes trajectories as composite Bernstein polynomials, uses the convex hull property to prove that obstacle avoidance and input constraints hold throughout the entire planning horizon, and runs in closed loop at online replanning rates, sending body velocities to the motion controller.
The study proposes a multimodal framework that uses large language models and vision-language models to translate human purposes expressed in natural language into robot missions and drives, aligning the behavior of open-ended learning autonomous robots with human intent. The system has three layers—semantic alignment, mission generation, and drive modeling—forming a bidirectional interaction loop in which human input defines high-level purposes, the robot expands them into executable missions, and feedback maintains alignment. Simulation and physical robot experiments show that the pipeline can generate effective missions and behaviors.
The study proposes a distributed predictive flocking controller in which each miniature vibration-driven robot independently solves a nonlinear model predictive control (NMPC) problem and exchanges only predicted trajectories with local neighbors, comparing six barrier and control barrier function (CBF) safety formulations on a digital twin of an open-source vibration-driven bristlebot in a 0.64-square-meter arena.
The authors propose the PIFR benchmark to evaluate preemption and resumption capabilities in long-horizon embodied navigation when a higher-priority instruction interrupts the current task sequence. PIFR includes single, repeated, and nested interruption settings, with stage-specific metrics for inserted-task completion, resumption of the original task, and completion of the full sequence. The authors also propose a lightweight candidate-ranking baseline to improve target re-grounding after interruption.
The paper proposes MATE-BT, a decentralized framework for heterogeneous multi-robot teams that hierarchically decouples mission execution, task allocation, and task execution. An onboard active leader manages mission progression and team-level task assignment, and authority transfers after a leader failure.
The paper proposes the VisGeo-PipeTrack servo control framework, which uses YOLOv5 to detect pipe labels and combines visual geometric constraints for localization, enabling UAVs to track pipes with dynamic motion such as vibration. On the control side, it adopts a model predictive control-based real-time corrected adaptive PID (MPC-RCAPID), which estimates the model online via recursive least squares and adjusts PID parameters through rolling optimization.
The authors propose Reward-Guided RRT*, a sampling-based framework that directly constructs budget-satisfying task sequences in continuous state space through a series of locally joint task-selection and motion-planning stages, without requiring a precomputed task-transition graph.
RetroNav is a training-free lifelong navigation framework that integrates navigation, memory accumulation, and failure reflection, achieving state-of-the-art performance on five navigation benchmarks. It uses a unified memory system to link geometric and semantic scene knowledge with historical navigation experience, distinguishing objective observations from task-dependent interpretations. After each navigation attempt, an independent evaluator determines whether a retry is needed; when it is, a retrospection agent uses scene evidence to generate a retry brief. The authors say the method will be open-sourced.
PushAround proposes a physics-informed hybrid search method that combines geometric connectivity reasoning with short-horizon simulation to generate executable clearing plans for mobile robot teams. The method uses repeated W-clearance connectivity graph (WCCG) queries to identify frontier gaps that need widening and ranks them by estimated downstream cost, then generates direct or recursive pushing candidates, selecting obstacles, directions, contacts, and forces.
LECO converts online trajectory optimization for dynamic target tracking into offline learning of a direct-control policy, using a differentiable kinematic model to let trajectory-level objectives directly guide control learning. Across three simulated obstacle densities, its root-mean-square linear jerk is 14.1%–31.2% lower than the best comparison method on this metric, it achieves the highest tracking success rate, and its per-cycle computation time is 4.25–5.63 ms.
The paper proposes an uncertainty-aware control barrier function (CBF) framework that approximates cascading covariance along multi-hop localization chains using Fisher information matrices, for UAV swarm search in GNSS-denied maritime environments. The framework incorporates the covariance model and its rate of change into constraint margins for connectivity maintenance and collision avoidance, and combines a hierarchical Voronoi target strategy with formation keeping to achieve forward coverage.
The authors propose ISB-NMPC, which couples spherical-bearing prediction with nonlinear quadrotor dynamics to jointly optimize target-approach motion and target visibility. Across 30 paired simulation trials, the method achieved an interception success rate of 86.7%, the highest among the compared controllers, with a mean per-trial target-visible time of 99.11%. In outdoor flights, the interceptor acquired a 30 cm target at approximately 150 m, reached 17 m/s in long-range flight, and flew at approximately 14 m/s while pursuing a maneuvering target towed by a quadrotor. Both software and hardware are open source.
This 35-page review divides networked humanoid robot intelligence into four functional categories based on information exchange: communication, coordination, collaboration, and social interaction with humans. It surveys the literature along six dimensions: interaction architecture, exchanged information, physical coupling, timescale, system scale, and embodiment.
A discrete-event simulation study compared two implemented intra-aisle robotic picking configurations: without an onboard tote lift, two level-captive shuttles serve the two tested levels; with the lift, one shuttle remains on its horizontal level and its onboard mechanism moves the order tote to the adjacent upper level to complete picks.
The authors propose OSMA-Bench, a benchmark that uses spatially matched scenario sets differing only in obstacle type to test whether robot navigation adjusts clearance and speed according to obstacle semantics, and they propose the Semantic Sensitivity Index (SSI) to measure this adaptation. Evaluating VLM and vision-language-action (VLA) policies on the benchmark, they find that even with explicit safety prompts, neither consistently adjusts its motion according to obstacle safety levels.
The authors propose ThinkLocoManip, a whole-body control agent framework for legged loco-manipulation that uses an interleaved reasoning-and-acting paradigm to handle long-horizon tasks in large-scale environments. The framework orchestrates perception, navigation, manipulation, and locomotion as composable tools, and it designs cross-domain coordination skills and a keyframe thinking mechanism to support error recovery. In real-robot experiments, its success rate was 77%, more than double the 37.1% of the strongest plan-then-act baseline.
The authors propose ScheNav, a closed-loop navigation framework that turns rough sketch guidance into exploration and stopping decisions. Its core is SchemaField, a learning module that fuses guidance with accumulated observations and predicts spatial value fields. A supporting data pipeline converts 2D floorplans into navigation training environments, producing the Furnished-RPLAN dataset, which contains over 33k furnished layouts with paired navigation episodes and noise-controllable sketches.
This paper proposes a closed-loop autonomous laparoscope control framework that couples real-time surgical phase recognition with camera-motion-compensated visual perception, using phase dependencies among surgical entities to improve target state estimation under occlusion and entity overlap. Surgical phase recognition and target tracking were evaluated on benchmark datasets, and phantom experiments showed improved target visibility and task execution performance compared with fixed-field-of-view control. The paper was submitted to IEEE Robotics and Automation Letters (RA-L).
RoboDuo proposes a closed-loop dual-system framework that uses code as the interface between a coding agent and two VLAs: the coding agent writes programs that let a full-task VLA attempt the entire task and check whether it is complete; on failure, an atomic VLA completes the remaining steps.
The authors propose the Dual-Stream Gated Network (DSGN), which separates surveillance and containment into independent representation streams and coordinates them through a context-aware gating mechanism to alleviate task-switching difficulties in large-scale robot networks. The method also introduces a probabilistic prior to regularize the gating process, reducing ambiguous intermediate decisions. Under constant-density cross-scale settings, DSGN maintains task effectiveness and scalability; ablation and staged experiments show that dynamic gating and the probabilistic prior support task arbitration and mode switching. The paper has been accepted to RA-L October 2026, and the code is publicly available.
The study proposes a real-time, LiDAR-only framework that learns interaction-aware path corrections from unlabeled demonstrations: constant-velocity extrapolation generates residual learning targets, a GNN models relationships among the robot, pedestrians, and goal context, and at deployment an extended Kalman filter tracks LiDAR detections to build dynamic graphs, with the GNN offsetting planner-generated waypoints through a planner-agnostic interface.
The paper proposes Extended LASMP, which incorporates navigation commands directly into the sampling distribution to improve the exploration efficiency of sampling-based motion planning for quadcopters in three-dimensional environments. The method generalizes subset sampling to command-aligned regions and uses a prompt-based large language model to parse natural-language instructions into yaw-conditioned and altitude-aware geometric constraints. Simulations in complex environments show that the method generates collision-free, consistent trajectories while significantly reducing sampling effort compared with uniform RRT-based planners.
The authors propose a task and motion planning (TAMP) framework that alternates task-directed interactive information gathering with planning and execution. It plans optimistically for interactive actions based on PDDLStream, verifies outcomes during execution, retains actions that remain applicable, and replans only the subsequent connections. The framework was tested in simulation and on a real robot on tasks such as determining whether an object is movable, whether a pen has ink, whether a drawer can be opened, and whether a container is empty. The results show that interaction-driven state updates and recovery replanning can support continued execution under uncertain outcomes and external disturbances.
The paper studies the problem of Sybil attacks during multi-drone collaborative exploration of buildings: a compromised drone forges identities, claims frontiers, and sends fabricated scans, causing real drones to miss areas and generating nonexistent walls in the map. The authors propose five defenses that rely only on the drones' own sensors and maps, including auditing map data, checking for a body's presence at an identity's claimed location, trusting only identities whose bodies have been observed, quarantining identities unobserved by any drone, and binding each observed body to a single identity.
The study proposes Scan2Instruction and PRPT-KS, which use a single iPhone scan to build a training environment and adapt a language-guided navigation policy without human demonstrations. Across five real environments, RealNav achieved an 85% real-robot success rate, compared with 49% for a baseline post-trained on human demonstrations; in unscanned areas of the same buildings, it still reached 82%, versus 41% for the baseline.
HARP is a handoff-aware recovery planner that enables the receiving UAV to actively acquire a target view during multi-UAV aerial tracking handoffs without interrupting the sender's ongoing coverage.
MOON is a framework that adds two lightweight modules to a frozen vision-language navigation (VLN) policy, with fewer than 1% additional parameters relative to the original policy, raising the navigation success rate of the frozen base by 5.2 and 6.0 percentage points on R2R-CE and RxR-CE, respectively.