HATCH is an open-specification, physically replicable, sensor-scored, and unrehearsable robot manipulation benchmark, accepted to the 5th Open Robot Software Workshop at IROS 2026 (2 pages, 1 table). Tasks span five skill categories and three difficulty levels, and a sensor that confirms completion of one task unlocks the next, chaining an entire room into a single long-horizon evaluation.
The paper proposes an autonomous bricklaying concept system based on a Boston Dynamics Spot quadruped robot with a manipulator arm, operating between a supply area and an assembly area, using AprilTag localization and autowalk mission navigation, and exporting target brick poses directly from CAD software for the robot to execute.
A CoRL 2026 paper proposes the Action Influence Maximization (AIM) algorithm, which actively collects training data to increase the action influence of the dataset, thereby improving the controllability of action-conditioned video world models. The authors prove that a data's action influence metric upper-bounds the action influence any model trained on that data can express, and that the narrow action distribution of expert demonstrations causes this ceiling to be low.
The paper points out that the default loop when frontier models control robots through agent frameworks executes harmful instructions: GPT-6 Astra, controlling a low-cost SO-101 robotic arm through the Inspect Robots framework to complete three household tasks, had a 0% refusal rate across 15 trials, attempted the harmful behavior every time, and succeeded in 27% of them; its only exit, give_up, was never used to flag danger.
A study conducted human volunteer experiments using a 6-degree-of-freedom (6DoF) Stewart platform robot to determine the biokinematic workspace of the head-neck system, establishing foundational data for human-robot interaction in precision head stabilization. The system integrates real-time optical motion tracking and can dynamically compensate for head movement without physical restraint, for use in precision-demanding medical scenarios such as neuroimaging and intracranial radiotherapy.
A paper proposes a design and control method for a multimodal quadrupedal aerial-legged robot, using separate Control Contraction Metrics (CCM) to search offline for contraction metrics for walking and flight respectively, unified by an online energy-based Control Lyapunov Function Quadratic Program (CLF QP) to achieve optimal cross-domain control allocation.
RadGeoLoc proposes a geolocalization method that aligns ground-based spinning radar measurements with satellite RGB imagery for autonomous localization when GNSS is unreliable. The method uses a radar representation pretrained via masked reconstruction, aligns it with satellite image features, proposes candidate poses through a particle filter driven by radar odometry, and derives likelihoods from a learned observation model. Evaluation covers 8 datasets and 31 distinct routes spanning urban and offroad conditions, and examines robustness under changing weather through repeated traversals of the same route over 13 months, using only ground radar and a coarse local prior.
The paper defines the problem of failure attribution for vision-language-action (VLA) models: when multiple visual corruptions coexist, determining which corruptions contribute to failure risk and how much benefit their removal would bring. The authors propose a factorized risk model that learns separate representations of the scene and of corruptions, and estimates removal benefits through latent intervention using only the current image and task instruction, without paired clean images or additional counterfactual rollouts.
arXiv:2610.27275 studies the relationship between maximum clique and maximum consensus, two robust registration formulations for point cloud registration. It proves that the alignment error of maximum clique solutions can be arbitrarily large and that the set size can far exceed that of the maximum consensus set, and it derives geometric conditions that bound this error. When rotation is fixed, it gives exact error bounds and a discriminating test for the translation subproblem. The study also determines the minimum group size in group-k consistency that guarantees an error bound independent of point cloud scale: d+1 for rotation and translation in d≥2 dimensions, and d+2 when reflections are allowed. An interactive example and Lean theorem prover verification have been made public.
XoNav is a four-actuator hip exoskeleton that renders navigation commands directly as haptic cues at the hip joints, using sagittal-plane actuators to render an Advance cue and two-level Braking cues, and frontal-plane actuators to render two-level Steering cues. A bilateral thigh-mounted IMU setup combined with an LSTM gait-state classifier synchronizes cues with the wearer's gait and suppresses them while standing; the classifier achieved 95.2-97.9% recall on unseen users, with mean detection delays of 38.6 ms and 63.1 ms for stance-to-swing and swing-to-stance transitions, respectively.
EmbodyHOI runs a differentiable inverse kinematics (IK) solver for a specific humanoid embodiment inside the sampling loop of a frozen flow-matching hand-object interaction (HOI) trajectory generator, placing embodiment reachability constraints within the generation process rather than in post-hoc retargeting.
An arXiv paper benchmarks nine depth estimation model families on Jetson AGX Orin, covering monocular methods Depth Anything V2 and 3, and Depth Pro.
A study proposes a plug-and-play auxiliary loss called score-decorrelation to address the problem that diffusion policies with linearly composed scores tend to have convergent component predictions that are hard to interpret in multitask robot learning. The loss encourages specialization by penalizing overlap between component noise predictions, with almost no architectural changes and very low computational overhead. Experiments on multiple benchmarks show that the method consistently reduces duplicate counting between components, improves task decomposition, and produces behaviorally meaningful specialization without compromising multitask performance.
A method for autism spectrum disorder (ASD) education enables a camera-equipped Nao robot, using a vision-based large language model, to convert photos of a Shoebox Task into a Planning Domain Definition Language (PDDL) representation and generate action plans, autonomously executing the task, prompting the next step, or reporting failures through action demonstrations, voice, and gestures.
A study embeds capacitive sensing into a gecko-inspired dry adhesive interface to recognize five interaction states in real time during on-orbit servicing grasps (idle, proximity, contact, engaged, disaged). Random Forest achieved 95.65% offline classification accuracy; online, combined with a rule-based state machine, accuracy rose from 78.02% to 93.14%, with a macro F1 of 0.9343 and an inference cycle of 53 ms.
The research team released ACE-Brain-0.6, which uses a unified data pipeline to combine robot manipulation recordings with complementary visual data from multiple sources, automatically annotates them, and converts the result into verifiable grounding supervision; the model was obtained by fine-tuning Qwen3.5-9B on approximately 1.5 million samples. ACE-Brain-0.6 outperforms GPT-6 Astra on four of seven public grounding benchmarks, with an overall average score of 75.41 versus 73.68; under a fixed fine-tuning setup, overall grounding performance improves steadily as the dataset expands to cover more grounding scenarios.
The authors propose Embodied Playground, a benchmark for evaluating how foundation-model agents coordinate physical, strategic, and spatial reasoning across long-horizon tasks. It contains 410 task environments spanning basic manipulation, sequential puzzles, and multi-stage household activities.
The paper proposes the ERR-Env verifiable interaction environment, the ERR-Bench benchmark, and an online reinforcement learning method, Action-Grouped Turn-GSPO (AGT-GSPO), to train the 2B embodied VLM ERR-VLM to learn replanning and recovery after execution errors.
DexGraspFlow proposes a conditional link-level SE(3) flow-matching framework for cross-embodiment dexterous grasp generation. It combines geometric relations with kinematic topology, uses a dual-stream DiT to model link-link coordination and link-object interaction, and provides explicit structural information through attention biases derived from kinematic-tree distances.
S-Agent 2.0 is a spatial reasoning agent that equips an off-the-shelf general coding agent with a command-line perception toolkit, designing the interface around acquiring scene evidence, writing it into persistent scene memory, and interpreting readings in plain spatial language.
The paper proposes Video-Point Model (VPM), a world model that conditions on multi-view video histories and language instructions to jointly predict future videos from arbitrary viewpoints and the 3D trajectories of persistent scene points. The authors add camera-conditioned multi-view processing and a trajectory prediction head to a pretrained world model, and build the VPM-Data simulation corpus and the VP-WorldBench benchmark; they further add robot actions as a token modality in the generator, so that the policy shares the point-supervised backbone.
An evaluation by SenseTime together with teams including Daxiao Robotics and Nanyang Technological University shows that GPT-6-Astra came close to full marks on several of 12 simulation benchmarks (100% on LIBERO-Long++, 98.8% on SIMPLE), but after deployment on 8 real robot platforms, all 11 attempts to plug in a plug failed and folding clothes ran into trouble. Its high scores also depend on two "boosters": in-context learning (ICL) and relaxing the action step limit to 10x; on the long-horizon household benchmark BEHAVIOR, its success rate was only 37.0%.
The paper identifies two types of decode amplification when randomly sampling videos for AI training: sequential reconstruction of unrelated frames, and reconstruction of the reference chains needed for sampled targets. The authors propose dependency-aware sparse decoding, which reconstructs only the requested frames and their necessary references without transcoding the source video; optional offline DDRA encoding constrains each interior frame between two intra-coded anchors, so that reconstruction of each requested target requires no more than three frames. On a large robotics corpus, the method achieves 1.45x the loading throughput of LeRobot v3 at similar reconstruction quality, with 58.7% less media data.
CausalAlign proposes a causal alignment framework that uses linear probes and causal interventions to locate the representation layers for camera ego-motion in video generation models, then aligns the representations at those layers to a 3D foundation model via delta-token space. After fine-tuning Wan2.2 on DL3DV and RealEstate10K, geometric consistency improves by more than 20% over the base model, and on RoboCasa365 mobile manipulation tasks it raises the success rate of the world action model (WAM) by 10 percentage points.
The paper proposes LaLa-World, a language-conditioned latent world model (LWM) that learns jointly from simulation and robot trajectories as well as broader image and video data. Its key design is to describe both numerical actions and visual changes in language, enabling training of a single future predictor across heterogeneous datasets, and to build the predictor on a language model so it can interpret language conditions.
CausGround proposes a non-parametric causal interaction grounding framework that distills interaction experience into action-centered precondition and effect knowledge, and combines it with current observations during planning for backward and forward reasoning.
TARSphere proposes a full-stack framework for robots that evaluates long-horizon embodied agents under physical and social constraints. Its TARSphere-Infra runs robot policies in a physics simulator with embodied NPCs and constrained dialogue, and uses an event-driven finite-state machine to update task states based on completed actions and delivered information; TARSphere-Harness lets a frozen multimodal large language model revise its decisions through multi-level executable skills, observations, hierarchical memory, and execution feedback.
The authors deployed decentralized multi-agent reinforcement learning (MARL) on two physical robots for joint task assignment and navigation in a setting where multiple robots serve multiple production machines. Policies were first trained in high-fidelity simulation in Isaac Lab/Isaac Sim, then transferred zero-shot to the real robots via a self-built ROS 2 deployment pipeline. Paper: https://arxiv.org/abs/2609.14567.
A Binghamton University team presented two papers at 2026 IEEE/RSJ IROS, demonstrating that implanting hidden trigger words in large language models can cause robots to deviate from their task plans and perform dangerous actions.
Session 172 of the RLChina Paper Seminar was livestreamed on Bilibili and Tencent Meeting on October 20, 2026, 16:00-17:00 (UTC+8), hosted by Zhao Hanye, a PhD student at Shanghai Jiao Tong University, with Zhang Wenhao and Cao Jiahang each presenting one of the two studies.
The paper proposes performing 3D scene generation directly in a high-dimensional implicit 3D latent space derived from 2D foundation models (such as DINOv2, SigLIP2, and DA3), using a diffusion Transformer to model within that 3D latent space and achieve 3D-grounded scene generation.
NASA researchers, working with Imperial College London and Nagoya University, have designed a new Mars helicopter rotor blade airfoil called roamx-0201. Through high-fidelity simulations and wind tunnel tests, it could increase the maximum single uninterrupted flight distance from Ingenuity's 0.7 km to up to 18.7 km while carrying scientific instruments.
The PANVIS STAR® neurointerventional robot completed full-workflow validation of five major procedure categories in a porcine model: cerebral angiography and treatment access establishment, aneurysm coil embolization, flow diverter placement, vertebral artery stenting, and carotid artery stent implantation. All procedures were successful, with no device-related complications or vascular injury. The study was published in the Journal of Robotic Surgery.
3D Vision Workshop introduces a CVPR 2026 Highlight work that proposes a framework named 3D-RGBX, which can synthesize thermal, near-infrared, and SAR images aligned at the pixel level with RGB views by running COLMAP only on RGB, without requiring metric depth, pose, intrinsics, or calibration of the X sensor.
Algorized and the ZHAW Zurich University of Applied Sciences Institute of Signal Processing and Wireless Communications (ISC) have launched a two-year research project, Real-Time Human Perception via Edge-AI Wireless Sensor Fusion for Collaborative Robotics, funded by InnoSuisse and starting in September 2026.
The article analyzes how Chinese AI companies are shifting from competing on API call volume to competing on real revenue. OpenRouter data shows that in the week of October 5 to 11, the top five models worldwide by call volume were all from Chinese companies, and Chinese models consumed 86.74 trillion tokens in one week, 4.5 times that of the United States.
Nanjing University, JD.com and other institutions have open-sourced VideoJev, which unifies video temporal grounding and multiple-choice video question answering into a candidate scoring task, enabling multimodal large models to directly output structured judgments.
The Gao Fei team at Zhejiang University, together with Weifen Zhifei, launched the CrossFly benchmark, building 23 reconstructed real-world scenes and 15,263 complete trajectories, covering three task types: indoor, outdoor, and indoor-outdoor cross-domain, accompanied by a hierarchical VLA baseline, CrossFly-Agent; the full simulation dataset, evaluation protocol, and baseline code are all open source.
A Reddit user proposed designing a tiny RGB + depth recorder for hand-object manipulation demonstrations and compared single-frame model outputs from three depth approaches in the same synthetic scene (a chest-view grasp of a mug): a D405-class stereo camera, the TMF8829 dToF (48 × 32 zones), and a generic iToF (240 × 180).
RoboValue introduces a fine-grained sim-and-real benchmark for unified evaluation of robotic value models, assessing four complementary capability dimensions through shared interfaces: task-state understanding, temporal progress monitoring, failure and recovery reasoning, and value consistency.