#Quadruped robots (105)

Quadruped Patrol Platform Adds Net Launcher and Smoke

@ErenChenAI · 82 views · 2026-10-22

SuperNav: An Agentic Navigation System for Any Task in Any Scene

General-purpose service robots need navigation systems that can handle diverse human requests in unfamiliar environments, combining task generality with scene generality. Some existing methods fine-tune multimodal large language models (MLLMs) to predict navigation actions, making their behavior dependent on the coverage of navigation training data and potentially limiting generalization to new requests and environments. Our key insight is to let the MLLM focus on interpreting requests, understanding scenes, and making decisions while preserving its general-purpose capabilities and delegating motion execution to navigation tools. To realize this idea, we introduce SuperNav, which equips a pretrained MLLM with a specialized agent harness without navigation-specific fine-tuning of the MLLM. Our harness supports these decisions with Navigation Skills, agent-oriented Tools for physical interaction, and task-progress and context management. A unified visual-point interface connects decision-making to motion by allowing the model to specify destinations directly in images and revise its decisions from execution feedback. Together, these components support sustained navigation across different task requirements and environments. SuperNav outperforms four evaluated baselines on instance-level, multi-object, and demand-driven tasks. Category-level evaluation on HM3D and deployment on a real quadruped robot further demonstrate its applicability across environments. Project Page: https://zju3dv.github.io/SuperNav/

Jinkai Zhang, Jingyi Xu, Yuanhong Yu · 353 views · Oct 8, 2026

Object NavigationEmbodied Agent

Oct 8, 2026

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Gemini 4 Argon for Robotics: The Developer Blog Goes Live

@keerthanpg · 132 views · 2026-10-13

Quadruped robotsBoston Dynamics

Ten Checkers for Agentic CAD: The Verification Loop to Run After Every Model Update

David Bar's ten checkers for agentic CAD - spec, motion, geometry, edge, manufacturability, fastener, hole, picture, accepted-problems and test-the-checkers - written in Python on build123d and run after every model update.

BLOG

Agentic CADCAD-as-Code

Agile and Generalized Legged Locomotion via Attention-Based Neural Map Encoding

Achieving agile and generalized legged locomotion across terrains requires tight integration of perception and control, especially under occlusions and sparse footholds. Existing methods have demonstrated agility on parkour courses but often rely on end-to-end sensorimotor models with limited generalization and interpretability. By contrast, methods targeting generalized locomotion typically exhibit limited agility and struggle with visual occlusions. We introduce a unified reinforcement learning (RL) framework for agile and generalized locomotion that incorporates a novel attention-based map encoder in the control policy. This encoder extracts local and global mapping features and uses attention mechanisms to focus on salient regions, producing an interpretable and generalized embedding for RL-based control. We further propose a learning-based mapping pipeline that provides fast, uncertainty-aware terrain representations robust to noise and occlusions, serving as policy inputs. It uses neural networks to convert depth observations into local elevations with uncertainties, and fuses them with odometry. The pipeline also integrates with parallel simulation so that we can train controllers with online mapping, aiding sim-to-real transfer. We validate our framework with the proposed mapping pipeline on a quadruped and a biped robot, and the resulting controllers demonstrate strong agility and generalization to unseen terrains in simulation and in real-world experiments.

Chong Zhang, Victor Klemm, Fan Yang · 421 views · Jan 13, 2026

Legged LocomotionReinforcement Learning

Jan 13, 2026

UniLM-Nav: A Unified Framework for Zero-Shot Last-Mile Navigation

Mobile manipulation requires a robot to navigate to a target object or receptacle and then perform intended manipulation. However, reaching the vicinity of the target does not guarantee a manipulation-ready base pose, a problem known as last-mile navigation. Prior methods for last-mile navigation either rely on manual pose annotation or task-specific training, limiting their scalability to open-vocabulary settings with fine-grained spatial constraints. We propose UniLM-Nav, a unified framework for zero-shot open-vocabulary last-mile navigation. UniLM-Nav decomposes last-mile navigation into view selection, task-conditioned affordance grounding, and geometry-aware base-pose reasoning, all resolved with a shared multimodal large language model (MLLM) backend. Specifically, UniLM-Nav first selects a reference view that best captures the target object or receptacle from recently collected observations. It then grounds task-relevant affordance point in the selected view and lifts the result into the robot-centric coordinate frame. Finally, conditioned on the grounded affordance, task context, and robot geometry, it infers a manipulation-ready base pose for the robot. We evaluate UniLM-Nav on the OVMM benchmark, where it outperforms the previous state-of-the-art method, MoTo, by 3.13 percentage points. Analyses show that the components of our method are crucial to final performance, and that the choice of MLLM also has a substantial effect. We further deploy UniLM-Nav on a Unitree B2 quadruped robot with a 6-DoF Unitree Z1 manipulator, validating its applicability to real-world mobile manipulation tasks.

Zhuofan Zhang, Tianxu Wang, Guoxi Zhang · 331 views · Jul 7, 2026

Mobile ManipulationLast-Mile Navigation

Jul 7, 2026

Wheels + Legs + Hierarchical RL: Behind ETH Zurich's 8.3 km Autonomous Delivery

A complete wheeled-legged system from ETH Zurich's Robotic Systems Lab (Science Robotics): the low-level locomotion policy, trained by privileged learning (PPO teacher, GRU student imitation) with CPG gait primitives and the state estimator removed, switches autonomously between driving, trotting, and knee-crawling, peaking at 5.0 m/s on flat ground; the high-level navigation policy outputs bounded Beta-distributed velocity commands at 10 Hz, consuming the LLC hidden state and a visited-position buffer for exploration. Trained in WFC procedurally generated navigation worlds with dynamic obstacles, it completed an 8.3 km delivery across Zurich's Glattpark with minimal intervention; in point-goal comparison it was the only method with zero collisions, planned in 0.34 ms (~3 orders of magnitude faster), and achieved a mechanical COT of 0.16 (53% lower, 3x faster than ANYmal).

Lee, Joonho, Bjelonic, Marko, Reske, Alexander · 530 views · May 3, 2024

Wheeled-Leggedhierarchical RL

May 3, 2024