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#Planning and decision-making
Oct 19Mon
  1. arXiv cs.RO55

    SPACE: Adaptive Collision-Aware Swarm Exploration Based on Pheromone Fields

    SPACE is a swarm exploration method inspired by ant foraging. On a synchronized shared map, it maintains an attractive frontier pheromone, a repellent exploration pheromone, and a fast robot-density field, providing local field guidance for robots. The authors evaluated it on sixteen home layouts from HouseExpo and eight campus floors from KTH, using up to two hundred fifty-six robots. At congested swarm sizes, SPACE achieved the lowest inter-robot contact rate on HouseExpo and lay on the empirical Pareto frontier on KTH.

  2. arXiv cs.RO55

    SidewalkBench: A benchmark for visual navigation on urban sidewalks

    SidewalkBench is a benchmark for visual navigation on urban sidewalks built on NVIDIA Isaac Sim. It provides GPU-accelerated, high-fidelity sidewalk environments, including procedurally generated and real-world scanned scenes, and adds event-driven pedestrian reactive behaviors. The authors evaluated 9 visual navigation models on 330 unit-test scenarios, 800 pedestrian-reactive scenarios, and 105 long-horizon scenarios. They found that pedestrian interaction and long-horizon robustness remain key bottlenecks for existing models, and that scaling up sidewalk training with synthetic data is a viable direction.

  4. arXiv cs.RO50

    TRAIN: A reinforcement learning framework for terrain-aware interactive navigation for quadruped robots

    TRAIN proposes a two-stage deep reinforcement learning framework that, through the TRAIN-Loco and TRAIN-Nav pipelines, enables quadruped robots to achieve terrain-aware navigation in unstructured terrain and human-populated environments. Benchmarks compared quadruped locomotion and robot navigation baselines, and hardware experiments verified its sim-to-real transfer potential.

  6. arXiv cs.RO38

    Multi-Agent Reinforcement Learning-Based UAV Path Planning for Obstacle Avoidance in Stochastic Environments

    This study proposes a centralized training with decentralized execution method based on multi-agent reinforcement learning for UAV obstacle-avoidance path planning in stochastic environments, improved from the model predictive control (MPC) idea. Each agent communicates only with the centralized planner, plans feasible paths through extended observations based on a distance-weighted mean field approach, and uses multi-step value convergence to improve training efficiency. Comparative, ablation, and real-machine experiments verified the method's effectiveness and generalization performance.

  9. arXiv cs.RO71

    Self-Building Labs: An automated system that builds and runs laboratories according to scientific goals

    The authors propose the self-building laboratory (SBL) system, which can design, configure, and operate laboratory setups based on high-level scientific goals. Starting from an empty room or an existing self-driving laboratory, it fills in the laboratory layout, digital twin, robot skills, and experimental workflows. The system was demonstrated on a variety of robots and setups, and can scale from a single workstation to a laboratory with multiple robots working together. Its end-to-end capabilities were validated through a real-world quantum-dot synthesis experiment. The authors say this shifts the role of human researchers from manually configuring and programming device automation to specifying scientific goals.

  10. arXiv cs.RO58

    Learning Robot Metareasoning: Adaptive Computation Budgets for Real-Time Control

    The arXiv paper Learning Robot Metareasoning proposes modeling the determination of the optimal computation budget as a semi-Markov decision process (SMDP), in which a single decision jointly determines the robot's action commitment duration and the time allocated to the solver. The authors trained a reinforcement learning policy conditioned on the current state, the executing plan, and low-cost planner statistics. Across three task types—ground robot navigation, agile quadrotor flight, and contact-rich manipulation—the policy adjusts its budget according to scene demands and outperforms fixed-budget baselines.

  11. arXiv cs.RO46

    TransNav: Transformer-Based End-to-End Static and Dynamic Obstacle Avoidance for Quadrotors

    TransNav proposes a Transformer-based end-to-end navigation method for quadrotors that achieves obstacle avoidance in environments with mixed static and dynamic obstacles. The method uses a patch-based point-cloud encoding scheme, integrates perception and navigation into a single architecture, and directly predicts a finite-horizon action sequence and executes the first action. It also introduces a lightweight point-mass dynamics model that optimizes trajectory-level costs through numerical gradient propagation, enabling unsupervised training without expert demonstrations.

  13. arXiv cs.RO53

    Embodiment-Aware Planning for Heterogeneous Dual-Arm Manipulation

    This study proposes an embodiment-aware planning method in which two Franka FR3 arms equipped with different simple grippers (a suction cup and a parallel-jaw gripper) cooperate to complete manipulation tasks that a single gripper cannot perform reliably. Given a desired object motion specified by a human demonstration, the planner optimizes arm-trajectory assignments over a finite horizon, determining segment by segment whether single-arm manipulation or a coordinated dual-arm handover is required, while jointly considering heterogeneous grasp feasibility, motion constraints, manipulability, and cross-gripper compatibility; it also computes globally consistent assignments and adds explicit switching regularization.

  15. arXiv cs.RO51

    SkyAgent proposes Revisable Target Memory (RTM) to improve continuous aerial search

    SkyAgent proposes Revisable Target Memory (RTM) to address the problem in continuous aerial search where, after evidence becomes contradictory, old candidates still trigger approach or verification actions (which the authors call false candidate persistence, FCP). RTM associates evidence with persistent candidates, suppresses repeated counting of correlated views, and lets candidates be revised among three states: Tentative, Confirmed, and Rejected.

  16. arXiv cs.RO51

    CoEvolve: A Self-Evolving Code Policy Framework for Humanoid Robots

    CoEvolve is a framework that represents team coordination as executable code over fixed robot motor skills. Through verifier-guided co-evolution, multimodal foundation models instantiate coordination strategies, physical verification evaluates each executed transition, and rejected behaviors trigger structured revisions starting from an accepted checkpoint.

  17. arXiv cs.RO54

    ORD-WM: Object-Centric Rigid Dynamics 3D World Model for Robotic Manipulation

    ORD-WM proposes a 3D world model that learns object-centric rigid-body dynamics for robotic manipulation tasks. It uses a vector-neuron variational autoencoder supervised by signed distance fields to encode each object point cloud into a geometry-aware latent representation composed of 3D vector features, and uses an action-conditioned dynamics model to predict per-object SE(3) motion increments. It then analytically applies the predicted rigid-body transformations to the current latent state rather than generating an unconstrained next latent state, thereby separating persistent geometry from pose changes.

  18. arXiv cs.RO47

    InstAgent: A Recursively Self-Improving Agent Operating System for Heterogeneous Robot Collaboration

    InstAgent proposes a recursively self-improving (RSI) agent operating system that uses agentic behavior trees (BTs) to explicitly model capability assignment, inter-robot dependencies, and handoff checks for heterogeneous robots, and updates collaboration programs from execution feedback while preserving successful revisions for reuse in later tasks. Experiments on 20 real-world multi-robot tasks with 3 high-level models show consistent gains in task success rate, first-attempt completion rate, and execution efficiency.

  19. arXiv cs.RO56

    What Makes a Good Symbol for Hybrid Discrete-Continuous Planning?

    The paper proposes defining symbol usefulness from the perspective of the solving algorithm: a good symbol should expose the discrete decisions that simplify solving the original planning problem. The authors first introduce the concept of solver-sufficient symbolization for finite-horizon piecewise-affine systems and design a counterexample-guided symbol invention algorithm, then extend it to optimization objectives whose energy functions are general neural networks. Experiments on simulated and real-robot tasks show that the resulting symbols reduce solving cost while maintaining solution quality, and improve interpretability and controllability.

  20. arXiv cs.RO45

    GR-VIP: Geometric representation-conditioned visuomotor policy for precise docking

    The study proposes GR-VIP, a goal-conditioned visuomotor policy that uses a visual encoder pretrained for relative camera pose estimation, conditioning action prediction on dense relational features before pose regression to preserve fine-grained cross-view information for learning corrective actions from demonstrations. In real-world docking tasks, GR-VIP achieves high docking success rates and millimeter-level terminal accuracy in both the training environment and unseen environments, with transfer requiring no additional training. Supplementary experiments show the policy can align with non-docking targets and complete docking under the low-light conditions tested.

  22. arXiv cs.RO55

    PASS-VLN: A Passability-Aware Benchmark and Model for Local-Command Vision-and-Language Navigation

    PASS-VLN is a passability-aware benchmark and model for local-command vision-and-language navigation (VLN). It holds the scene, instruction, start pose, and reference route fixed while varying only the robot body width, requiring the robot to reach the target when the passage is feasible and to stop early when it is blocked.

  23. arXiv cs.RO48

    Shape-Aware Neural Time Fields: Physics-Informed Neural Time Fields for Rigid-Body Motion and Manipulation Planning

    We propose Shape-Aware Neural Time Fields, a physics-informed neural motion planning framework that learns a reusable travel-time (cost-to-go) representation for rigid-body motion, explicitly distinguishing the different geometric constraints on translation and rotation, and pairs it with a sampling-based planner.

  24. arXiv cs.RO55

    Study on Coding Agents Automatically Designing Robot Spatial Memory: Codex / GPT-6 Astra Five-Track Average Improves 50.9% Over ReMEmbR

    The arXiv paper "Spatial Memory by Agents, for Agents" investigates letting coding agents automatically design robot spatial memory: under a fixed development budget, a Designer Agent writes and iteratively revises memory construction and retrieval code, with feedback from a fixed Query Agent; the resulting implementations are frozen and then used to build memory from held-out observation histories for evaluation.

  25. arXiv cs.RO41

    Task Structure and Scalability in Multi-Robot Drawing: A Simulation Comparison of Single-Token and Shared-Token Allocation

    A simulation study compared the speedup effects of two pipelines for multi-robot drawing: single-token (component-level) allocation and shared-token (decomposed) allocation, covering 385 drawings and team sizes of 1 to 8 robots. The results show diminishing speedup gains under single-token allocation; the shared-token pipeline achieved higher mean speedups and lower idle-time shares at larger team sizes, but it did not shorten completion time for every drawing.

  26. arXiv cs.RO76

    RAX: An open-source framework for scaling robot agent capabilities through online exploration

    The paper proposes RAX (Robot Agent eXploration), an open-source robot agent framework centered on scaling online exploration, in which a general-purpose coding agent generates robot control code without using action demonstration data, hand-engineered action primitives, or value-function guidance.

    Why it matters: The paper compares the imitation learning paradigm that relies on demonstration data with a demonstration-free online exploration approach, and reports quantitative gaps in simulation and on real robots, helping readers assess the feasibility of this direction.

  27. arXiv cs.RO45

    Mothership: A Two-Tier Autonomous Refuse Collection System for Pedestrian Campuses

    Mothership proposes a two-tier autonomous refuse collection system in which an electric compaction truck carries two low-speed sidewalk robots, RoboCarts, that fetch and deliver standard two-wheel bins to the truck, return them to their positions, and re-dock for charging. A discrete-event simulation over synthetic campus layouts at 2-16 bins/ha quantified throughput: a two-cart configuration serves 20-29 bins/h, while three to four carts can reach 35-49 bins/h at an 80 m docking radius; the optimal docking radius must be chosen together with fleet size.

  29. 仙工智能58

    Seerbotics demonstrates VLA model completing Tower of Hanoi task: still able to continue solving after base is pushed and discs are scrambled

    Seerbotics demonstrated its self-developed VLA model driving a robotic arm to complete the seven-move sequence of a three-disc Tower of Hanoi. When an observer slides the base to change its overall position or rearranges the discs into an arbitrary intermediate state, the robotic arm can still re-identify the current configuration, determine the next move, and continue completing the remaining task. Trained on only 60 demonstration trajectories, the model can generalize across changes in object position, color, and shape. The Seerbotics robot "brain" consists of three layers—model, cerebellum, and nerve—responsible respectively for scene understanding and decision-making, motion planning and execution, and a unified interface connecting sensors and joint actuators.

Oct 18Sun
  1. Guyuehome58

    τ₀-VLA breakdown: Turning subtask decisions into a scalable reasoning problem

    The article breaks down τ₀-VLA, a hierarchical robot foundation model open-sourced on July 27, 2026 by Luo Jianlan's team at the Shanghai Innovation Institute (SII). The model targets long-horizon mobile manipulation on the order of ten minutes in home scenarios, such as meal prep, folding and storing clothes, and tidying across rooms. Its core idea is to turn "what should the next subtask be" into a reasoning problem that can be scaled with more compute.

  2. Guyuehome48

    AirCombat: Tactical Decision-Making and Swarm Engagement Simulation for Multi-UAV Air Combat

    AirCombat uses rule-based agents to simulate UAV air combat swarm engagements ranging from 1v1 to 10v10, covering close-range dogfights and ten-versus-ten scenarios. The system breaks air combat decision-making into 48 discrete actions and introduces an abstract weapon engagement model and a formation target assignment mechanism to explain the sources of tactical decision-making complexity in multi-aircraft confrontations.

Oct 17Sat
  1. 深蓝AI · 微信公众号50

    Zhejiang University's GuardPIBT enhances PIBT with residual reranking, completes conflict-free 3D planning for 1 million agents in simulation

    A Zhejiang University team proposed GuardPIBT, which, while retaining the PIBT execution kernel, uses a neural network to make residual reranking proposals over PIBT's native candidate actions; conflict checking, priority inheritance, and backtracking are still handled by the PIBT kernel, and counterfactual grouped gating is introduced to filter harmful interventions.

  2. 锐曼REEMAN42

    How the REEMAN Fly Boat AMR transports 200 kg of cargo via a 1.15 m elevator: a real electronics factory case study

    The REEMAN dual-laser Fly Boat factory delivery robot (Fly Boat AMR, ABOT11B) is deployed at an electronics factory in Zhuhai, transporting about 200 kg of PCB materials and bottled water per trip, with a rated payload of 300 kg, a loaded delivery route of about 600 m plus an empty return of about 100 m, and the customer reports about 70–90 trips per day.

Oct 16Fri
  1. Reddit r/robotics39

    LLM agent drives an AMR using Husky A300 + ROS2 Nav2 + local Qwen3

    A developer on Reddit demonstrated driving a Husky A300 mobile robot with a local Qwen3 LLM agent through ROS2 Nav2: it uses an asynchronous background task architecture, so chat still responds during navigation, and llama.cpp was compiled with CUDA 12.4 for real-time local inference with no cloud dependency. Spatial coordinates are kept in the conversation context with no external database; it currently runs entirely in simulation, and the same action-client/function-calling architecture can be transferred directly to a physical robot.

  2. PaperWeekly · 微信公众号54

    Beihang KDD 2026 paper proposes MoE-Pointer, using Seq2Seq reinforcement learning to jointly optimize rider-drone relay delivery

    Professor Wang Jingyuan's team at Beihang University, together with the School of Management at Shanghai University, published the paper MoE-Pointer at KDD 2026, defining the Dynamic Multi-Stage Pickup and Delivery Problem (DM-PDP) and using a sequence-to-sequence generation framework to achieve end-to-end reinforcement learning joint optimization of a three-stage rider-drone delivery chain.

  3. Robotics & Automation News55

    Pudu Robotics showcases D7 semi-humanoid and D5-W quadruped at IFA 2026, expanding beyond service robots

    Pudu Robotics made its debut at IFA 2026 in Berlin, showcasing a product line expanding into semi-humanoid and quadruped robots. The D7 industrial semi-humanoid robot was shown in Europe for the first time, with 23 joints concentrated in the arms, waist, and head rather than the legs, and can carry a 20 kg payload; the D5-W quadruped robot combines wheels and legs, can carry 30 kg and climb stairs, switch between wheeled and legged locomotion, and recover autonomously after falling; the CC1 Pro cleaning robot adds the ability to self-check cleaning results and rework them.

  4. 中国机器人网 · 微信公众号82

    Google-owned Intrinsic open-sources robot foundation software platform Intrinsic Core

    At ROSCon 2026 in Toronto, Canada, Intrinsic announced that it is open-sourcing the core components of its industrial robotics platform under the Apache 2.0 license and launching Intrinsic Core, a ROS-compatible foundation software platform.

    Why it matters: The article clearly lists the control, perception, and planning components included in the platform and their open-source license, so readers can judge which repetitive foundation software work it can replace.

  5. HuggingFace Daily Papers47

    What Kinds of Symbols Are Suitable for Hybrid Discrete-Continuous Planning?

    The paper proposes defining the usefulness of symbols with the solving algorithm at the center, introduces the concept of "solver-sufficient symbolization," and develops a counterexample-guided symbol invention algorithm for finite-horizon piecewise-affine systems. The method can be extended to optimization objectives that use neural networks as energy functions, inducing solver-sufficient symbols from continuous optimization landscapes. Simulations and real-robot task experiments show that the resulting symbols reduce solving cost while maintaining solution quality, and improve interpretability and manipulability.

  6. HuggingFace Daily Papers56

    StopWatch: A Goal Grounding Approach for Object-Goal Navigation

    The StopWatch paper diagnoses that the main reason success rates remain low after fine-tuning pretrained VLMs into navigation policies is a lack of goal grounding: the policy often fails to notice that the goal is already in view or struggles to estimate its distance. The authors propose an explicit grounding method that achieves state-of-the-art results on six benchmarks across three scene collections, and they correct annotation defects in these benchmarks. Analysis on the corrected benchmarks shows that, with ground-truth grounding, the policy nearly reaches the level of the expert that generated its demonstrations, indicating that the bottleneck is perception rather than policy learning.

Oct 15Thu