Autel Robotics launched the AAGI (Autel Air-Ground Intelligence) solution brand in Shenzhen, and together with Daohe Tongtai and Saifang Technology introduced the AIP foundation along with three industry application platforms: EIS 2.0, Avant Hub 2.0, and Skyshield 2.0.
REEMAN built a custom food delivery robot based on the Hot Wheels robot chassis (model WBOT11B), featuring a two-layer transparent enclosed compartment and supporting single-point and multi-point autonomous delivery as well as automatic charging at low battery.
The VMR "Jixing" V400 point-to-point transport robot from Weimaier, a subsidiary of RoboCT, will make its debut at CeMAT ASIA 2026 at Booth C3-3, Hall N1, Shanghai New International Expo Centre. The product can complete basic mapping and deployment in as little as 30 minutes; users can set waypoints, adjust routes, and dispatch tasks via a visual app. It uses 3D SLAM to automatically build environment maps for autonomous navigation and creates safety zones through 3D spatial perception, choosing to pass, detour around, or wait for obstacles.
The REEMAN Ironhide 3.0 is a 1.5-ton laser SLAM AMR forklift that can autonomously identify pallets, position its forks, lift and transport loads, and place goods without reflectors or floor tape. In a case at a new energy factory in Huzhou, it completes about 160–200 pallet transports per day along a 47-meter route, runs continuously for 6 hours fully loaded and 8 hours empty, and supports 1-hour fast charging and automatic return to charge.
LOGIV is a task-graph framework for long-horizon robot tasks that connects symbolic planning, observation-based verification, and embodied execution. During execution it checks the physical validity of condition-support and resource-ordering edges, and after a failure it uses dependency tracing to determine which parts must be restored.
OpenFlyV2 is a unified onboard vision-language-action architecture that uses a shared vision-language representation to support semantic understanding, language-grounded perception, spatial reasoning, and continuous flight control within a single model. The authors built a simulation dataset of approximately 70,000 navigation trajectories and 60,000 vision-language instruction-response pairs. On an NVIDIA Jetson Orin NX, the model runs fully onboard and generates action chunks of 12 waypoints each at 10 Hz.
Based on the dynamics-aware diffusion-denoising framework D4orm, this paper builds a family of multi-robot trajectory planners for different operational requirements, including a decoupled planner, an online receding-horizon planner with feedback control, and a distributed planner. Evaluations with differential-drive and holonomic robots in 2D and 3D environments show that D4orm-based methods find high-quality solutions faster and more reliably than sampling-based optimization methods such as MPPI and a learning-based diffusion model method.
The authors propose DR-LfD, a framework that integrates visuomotor policies into a TAMP-gated decision-making system, decomposes human demonstrations into atomic skills based on contact relationships, and then reproduces and reorganizes them using visuomotor policies or object-centric primitives. This method shifts the demonstration data requirement from growing exponentially with the skill sequence to growing with the number of skill types, and on two DexMimicGen tasks it outperforms a diffusion imitation policy trained on 1,000 demonstrations while using only 100 demonstrations. The paper has been accepted by T-RO.
This research uses manipulator motion for propellant-free attitude control of free-floating spacecraft, modeling it as a trajectory optimization problem with joint and collision-avoidance constraints and solving it with an interior-point method. Across a range of spacecraft-manipulator systems, the method computed rest-to-rest slew trajectories of up to 90° and detumble trajectories with a 40% reduction in angular velocity. The authors also directly compare the manipulator's control authority with that of reaction wheel arrays via momentum and torque envelopes, noting that manipulators could serve as a redundant or even primary attitude control system, with particular promise for in-space assembly and manufacturing involving the capture of high relative-mass payloads.
The authors propose SPARK (Sequential Planning via Anchored Robotic Keypoints), which describes tasks as a symbolic behavior tree, anchoring to the current 3D observation only when each action is invoked; it verifies the plan at runtime and re-anchors objects on local failure, requesting a new plan only if this step fails.
VEGA proposes a method for training navigation Vision-Language-Action (VLA) models from unlabeled egocentric navigation videos. It reconstructs local scene geometry from monocular video, samples text, image, and spatial waypoint goals, and generates obstacle-avoidance trajectories, then uses these trajectories to train a flow-matching VLA navigation policy; geometry is used only during training.
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.
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%.
WildOS is a unified system for long-range, open-vocabulary object search that combines safe geometric exploration with semantic visual reasoning for complex outdoor environments without prior maps and with limited depth sensing. The system builds a sparse navigation graph to maintain spatial memory and uses a foundation-model-based vision module, ExploRFM, to score the graph's frontier nodes, simultaneously predicting traversability, visual frontiers, and object similarity to enable real-time onboard semantic navigation.
An arXiv paper compared the performance of a centralized commander, an auction-based consensus team, and independent LLM agents under five network conditions in a simplified perimeter-defense simulation. The consensus team had a 15% breach rate, the commander 32%, and independent agents 29%.
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.
The authors propose ID-WM (Implicit Dynamics World Models), a hierarchical world model in which a high-level model samples feasible transitions on the state manifold and a low-level world model acts as an inverse dynamics solver to ground those transitions into precise actions. On three navigation and manipulation test suites, it improves over existing policies and diffusion planners by 38.5%, and by 135% over single-layer world model MPC in complex unseen long-horizon configurations.
The authors propose REA, a routed embodied agent runtime that lets a reasoning-focused System 2, an embodied router System 1, and reactive System 0 policies cooperate within a single runtime, with the core rule that a running policy never waits on the brain.
A study proposes a hybrid path planning framework for multi-goal navigation of assistive wheelchairs, using the Traveling Salesman Problem (TSP) for global task ordering combined with the Potential Field (PF) method and heuristic local planners such as RRT and A* to achieve collision-free navigation, and develops two algorithms, TSP-PF-RRT and TSP-PF-A*. In Monte Carlo simulations of crowded and super-crowded scenarios, the framework achieves efficient multi-goal navigation in dynamic human environments containing heterogeneous static and dynamic obstacles, with TSP-PF-RRT producing fewer or zero collisions and shorter travel distances.
The study proposes Proof-Carrying Evolution (PCE), in which a language model provides both a controller and its certificate during search; only candidates that pass certificate verification can be returned, and the deployed artifact is the control law together with its certificate. The authors compare four language models with classical search and synthesis methods on speed-limited docking, maneuvering-target pursuit, and SO(3) attitude control. Certificates for linear problems are verified using exact rational arithmetic; on SO(3), floating-point verification is followed by exact re-verification of each reported champion. The best run of each method also receives a Lean 4 proof.
The study extends two algorithms, CADENCE and its decentralized version DADENCE, to the dynamic version of POCGAGP (DPOCGAGP), adding two deployment-time perturbation settings, Agent Loss and World Change, for multi-agent systems to establish a fully covering communication network in unknown constrained spaces. On more than 4,500 benchmark test cases of varying scale and structure, the improved algorithms outperform another state-of-the-art benchmark algorithm. The work focuses on simulation benchmark evaluation and has not yet involved real-robot validation.
This research models low-thrust (LT) orbital transfers within a partially observable Markov decision process (POMDP) for on-orbit servicing (OOS) mission planning, adding the servicing spacecraft's remaining propellant to the state space to support mass updates. High-thrust transfers are modeled with a precomputed transfer matrix, while low-thrust transfers are represented by an analytical GEO phase model, with propellant consumption and maneuver feasibility computed for given transfer-duration alternatives. In a simplified OOS scenario, SARSOP is used to solve and compare high-thrust and low-thrust policies, quantitatively assessing the feasibility and operational impact of the low-thrust option.
The authors propose a decentralized motion planner based on model predictive path integral control that jointly computes the ego vessel's trajectory and predictions of other vessels' trajectories, encoding traffic rules as discontinuous costs without requiring smooth approximations. In maps taken from real sections of Amsterdam's canals, 780 randomized runs all reached their goals without collisions or deadlocks, outperforming an optimization-based planner designed for the same domain as well as a version of the proposed planner that does not account for interaction. Two scaled vessel prototypes completed head-on and crossing encounters without rule violations, planning and localizing onboard at 10 Hz in an outdoor environment.
The paper derives a complete analytical characterization of optimal collision-free trajectories for two robots moving from rest to rest under double-integrator dynamics and a squared-acceleration objective. It proves that these trajectories belong to eight contact families and gives the family determination for each combination of initial and final positions. The authors analyze the geometric properties of the optimal trajectories, provide a numerical computation algorithm, and discuss how these results can improve the performance of existing numerical trajectory optimizers.
The authors propose the sequential contact insertion (SCI) algorithm, which organizes optimization around contact points where robots reach the minimum allowed separation and analytically reconstructs trajectories between contacts. In numerical experiments, the method can plan for one million robots within minutes, while thousands of robots take less than one second; on problems solvable by a fixed-grid sequential convex programming baseline, SCI achieves median speedups of several orders of magnitude with comparable solution accuracy.
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.
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.
BioAgent is an embodied agent for long-horizon laboratory protocols that acquires new skills by matching human-reviewed requirements with validated skills, selecting tool-based implementations for missing behaviors, and requesting human demonstrations as needed. On the second task, it reused 17 skills unchanged, modified 2 existing implementations, and added 5 new skill types; on two dual-arm laboratory tasks with 20 and 31 ordered skill invocations respectively, it achieved complete-task success rates of 66% and 58%, with each task run 50 times and autonomous recovery enabled.
The authors propose HIPC, a real-time humanoid iterative planning and control framework that couples a generative model with motion tracking into a unified planning-and-control paradigm. HIPC uses goal-conditioned motion planning and trains with a canonical motion representation to avoid gimbal-lock singularities under extreme poses, while mitigating out-of-distribution shifts caused by real-robot state feedback through two strategies: deviation-decayed recovery and history re-anchoring. Experiments show that HIPC achieves the best goal-reaching accuracy among existing methods and exhibits robustness to physical disturbances and the ability to recover after falls.
The paper proposes the Hourglass architecture, which decomposes cross-embodiment motion planning into a body-agnostic world model and each robot's differentiable ego-dynamics template, coupled only through a camera ego-motion interface. On seven simulated robots across five motion types, this generalist policy achieves success rates 3.8–10.6 percentage points higher than imitation learning and diffusion policy baselines on the same world model, matches each robot's specialized policy without dedicated training, and comes within 1.4–2.8 percentage points of a parameter-conditioned MPPI teacher.
The authors propose CONSORT, a multi-robot planning method for cooperative localization in GPS-denied environments. By optimizing only over mean-state trajectories and propagating covariance only for terminal constraint evaluation, it turns the belief-space problem into a solvable form.
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.
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.
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.
A joint team from Fudan University's Institute of Trustworthy Embodied Intelligence and Shanghai Xinzhi Embodied Intelligence Technology Co., Ltd. won first place in the quality inspection category at the 2026 International Embodied Intelligence Skills Competition. The student team, working with the National and Local Center, used the Linglong H wheeled-arm robot and the Linglong L bipedal robot to win two first places, one third place, and one fourth place across three categories: loading and unloading transport, firefighting, and precision assembly. In the firefighting category, the Linglong L bipedal robot completed fire detection, fire suppression, and evacuation without human intervention.
Dong Hao's team at Peking University has published the paper "One Scan is Enough," which uses a LiDAR-equipped iPhone to scan a target environment once, automatically generates navigation tasks, and uses the reconstructed scene geometry for reinforcement post-training, without needing manual teleoperation demonstrations.
Why it matters: A single iPhone scan replaces manual demonstrations; 200 real-robot tests give a success-rate comparison from 25% to 85%, showing how post-training costs could fall.
Xingyan Intelligence has launched Auriga, a general embodied Harness framework. Under a zero-shot protocol that involves no task-specific pre-tuning, no reliance on expert demonstrations, single-attempt execution, and no environment resets, it achieved a 57.74% average task success rate and an average process score of 64.00 across 42 tasks and 6,300 test episodes on RoboDojo, using GPT-6 Astra as the base model. It leads the leaderboard on both metrics.
Fysics AI, together with Fudan University's CITLab, proposed the unified world-action model Fysiverse-UniWAM, which uses a single model to handle autonomous navigation, mobile manipulation, and fixed-workstation dexterous manipulation.
Why it matters: Readers can see a unified world-action model approach that does not rely on large-scale robot pretraining, along with the specific design of a shared backbone for navigation and manipulation.
Xinzhiyuan reported on the SpatialScout benchmark, which gives a camera to an AI and places it in previously unseen virtual rooms, where the model autonomously decides when to turn, move, and capture images, and submits the 3D coordinates of target objects. The formal evaluation selects 80 indoor scenes and 3,140 target instances from iTHOR, RoboTHOR, ArchitecTHOR, ReplicaCAD, and ProcTHOR, with at most 100 interaction rounds per task; scoring considers both mean IoU and the proportion of IoU above 0%, 50%, and 90%.
On November 1, the bipedal robot "Hangxing No. 2," developed by DEEP Robotics in collaboration with Hangzhou public security traffic control authorities, made its debut at the 2026 Hangzhou Marathon, stationed at 4 key points around the Huanglong Sports Center to handle tasks such as intersection monitoring, autonomous patrols, dissuading traffic violations, and answering public inquiries.