0:10ElectroPup: a 3D-Printed Open-Source Robot Dog@Alacritic_Super · 117 views · 2026-09-07Quadruped robotsOpen SourceDIY Robotics
0:15DeepRobotics Pulse firefighting robot dog@VisiveAI · 98 views · 2026-09-05Quadruped robotsDeepRoboticsFirefighting
1:27Teaching Robots Like Dogs@TaerimYoon_ · 204 views · 2026-02-09Quadruped robotsAgile Navigationhuman-in-the-loop
0:39Open-Source AR Interface Commands a Unitree Dog by Hand or Voice@JohannesTscharn · 216 views · 2026-09-04LenslistARAugmented Reality
0:55VOGEBOT: A Rideable Wheeled Quadruped@clankrmedia · 279 views · 2026-09-03VOGEBOTWheeled QuadrupedQuadruped robots
0:53Petoi Quaddle: A Do-It-All Mini Robot Dog for Physical AI@PetoiCamp · 738 views · 2026-09-03PetoiPhysical AIArduino
Contact-Guided Exploration for Non-Prehensile Locomanipulation with Multi-Critic RLThe paper turns 25 grasp-algorithm candidates into a retractable exploration objective, separates task, exploration, and regularization signals with three value heads, and schedules their advantages during training. The full method reaches 94.1% simulation success with 4.4% tipover and transports unseen IKEA objects in 40/58 zero-shot hardware trials. Simone Tolomei, Mayank Mittal, Franco Angelini·Aug 28, 2026Multi-Critic RLIsaac LabSim-to-RealAug 28, 2026
ABot-N1: Toward a General Visual Language Navigation Foundation ModelABot-N1 is a general visual-language navigation foundation model built on a slow-fast dual-system architecture: a 4B slow VLM reasoner emits explicit chain-of-thought plus dual pixel goals (Target Pixel + Affordance Pixel), while a 2B fast action expert decodes continuous SE(2) waypoints via QFormer action queries. The unified pixel-goal interface covers five tasks — point-goal, object-goal, POI-goal, instruction-following and person-following — in a single 30M-sample multi-task checkpoint, further aligned by GRPO post-training with format/target/safety rewards. Two closed-loop benchmarks are released (ABotN-PointBench and ABotN-POIBench). ABot-N1 sets new SOTA on all five benchmarks, boosting POI entrance arrival to 77.3% (+35.0 pp) and reaching 92.9%/95.4% outdoor/indoor point-goal SR, with full deployment on the TuTu quadruped running on a Jetson AGX Orin.Ruiyan Gong, Yingnan Guo, Junjun Hu·Jul 11, 2026VLNNavigation Foundation ModelSlow-Fast ArchitectureJul 11, 2026
1:23DEEP Robotics Lynx M20S transports fresh grapes in a 50°C vineyard@DeepRobotics_CN · 182 views · 2026-08-28Quadruped robotsLynx M20SAgriculture
0:38HFUN-TECH Transformer Humanoid: Autonomously Reconfigures Between Wheel-Foot and Quadruped@LeoKharon · 102 views · 2026-08-28Transformer RobotHumanoidQuadruped robots
DECOWAM: Decoupled Whole-Body World-Action Model for Legged Mobile ManipulationDECOWAM adapts a frozen FastWAM video-action backbone to legged mobile manipulation via decoupled interfaces — an action-equivalent future bottleneck, adversarial base/arm factorization, and ego-motion-aware video conditioning — cutting Stage-2 trainable parameters 232x while leading real-robot deployment at 58.2% success.Siyuan Ma, Boshi Zhang, Yutian Zhang·Aug 20, 2026VLAWorld ModelsWorld-action modelsAug 20, 2026
Motion Priors Reimagined: Adapting Flat-Terrain Skills for Complex Quadruped MobilityReinforcement learning (RL)-based motion imitation methods trained on demonstration data can effectively learn natural and expressive motions with minimal reward engineering but often struggle to generalize to novel environments. We address this by proposing a hierarchical RL framework in which a low-level policy is first pre-trained to imitate animal motions on flat ground, thereby establishing motion priors. A subsequent high-level, goal-conditioned policy then builds on these priors, learning residual corrections that enable perceptive locomotion, local obstacle avoidance, and goal-directed navigation across diverse and rugged terrains. Simulation experiments illustrate the effectiveness of learned residuals in adapting to progressively challenging uneven terrains while still preserving the locomotion characteristics provided by the motion priors. Furthermore, our results demonstrate improvements in motion regularization over baseline models trained without motion priors under similar reward setups. Real-world experiments with an ANYmal-D quadruped robot confirm our policy's capability to generalize animal-like locomotion skills to complex terrains, demonstrating smooth and efficient locomotion and local navigation performance amidst challenging terrains with obstacles.Zewei Zhang, Chenhao Li, Takahiro Miki·May 21, 2025ANYmalQuadruped robotsMotion priorsMay 21, 2025
1:11Quadrupeds Learn Real Dog Motions@leggedrobotics · 105 views · 2025-09-23Quadruped robotsANYmalMotion priors
DELTA: Deformable Elevation-Based Local Terrain Attention Encoder for Sparse-Terrain Quadrupedal LocomotionStable quadrupedal locomotion on sparse terrain requires selecting state-relevant terrain evidence for precise foot placement. Model-based foothold planners provide precise foothold selection but rely heavily on explicit model assumptions. Recent attention-based map encoding (AME) studies show that end-to-end reinforcement learning (RL) can learn implicit foothold guidance. However, the computational cost of dense AME encoding grows with map resolution, limiting its scalability to fine-grained sparse terrain. We propose DELTA, a Deformable Elevation-Based Local Terrain Attention encoder. DELTA predicts state-conditioned sampling locations, forms terrain evidence tokens from adaptive local elevation patches, and attends only to a fixed-size token set. With fixed sampling and patch settings, DELTA's encoder cost is independent of map resolution. Experiments show that DELTA achieves final traversal performance comparable to AME at the standard resolution while improving learning efficiency. This fixed encoder cost enables the use of higher-resolution terrain maps, improving traversal on fine-grained sparse terrain. DELTA also demonstrates strong generalization to unseen mixed evaluation courses composed of continuous and discrete terrain elements. Beyond simulation, DELTA demonstrates successful sim-to-real transfer on RAIBO2. Analysis of the learned sampling offsets and attention weights shows that DELTA samples steppable regions and attends to terrain evidence relevant to future touchdowns without foothold labels or attention supervision.Sanghyun Park, Moonkyu Jung, Jemin Hwangbo·Aug 22, 2026Sim-to-RealQuadruped robotsLocomotionAug 22, 2026
0:11China Galileo Robotic Dog: Transformer-Like Shape Shifting@XueJia24682 · 140 views · 2026-08-23Robot DogShape-ShiftingReconfigurable Robot
0:55Deep Robotics robot dogs help new students move in@DeepRobotics_CN · 137 views · 2026-08-22DEEP RoboticsQuadruped robotsRobot Dog
Learning Agile Navigation in Crowded Environments for Quadruped RobotsNavigating dynamic and crowded environments presents significant challenges for quadruped robots due to severe sensor occlusion and unpredictable human motion. Existing approaches face a trade-off: model-based methods, such as Velocity Obstacles (VO), theoretically guarantee safety but rely on accurate obstacle motion estimates that often fail in dense crowds, while end-to-end learning methods offer robustness but lack motion prediction capability of obstacles, leading to collisions or conservative behaviors. To solve this, we propose VOP-Nav, a novel navigation system that combines the geometric safety of VO with the agile adaptability of end-to-end learning. Using only local onboard observations, our system avoids explicit obstacle detection and tracking pipelines. The VOP-Net processes multi-frame LiDAR data to implicitly encode dynamic constraints and predict a safe velocity region derived from Velocity Obstacle theory. Importantly, the VO predictions serve a dual role: they are used as input to the navigation policy during inference and as a reward signal during training to encourage safe motion. Evaluations in Isaac Gym demonstrate that VOP-Nav achieves higher success rates than all baselines while balancing locomotion speed and collision avoidance. Real-world deployment on a Unitree Go2 quadruped robot further validates the system's robustness and efficiency in complex indoor and outdoor dynamic environments.Shuyu Wu, Zeyu Liu, Tianbao Zhang·Jul 16, 2026Quadruped robotsNavigationCrowded EnvironmentJul 16, 2026
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