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Robotics

Discover robotics research, automation, and intelligent systems

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Wed, May 20

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📰 article

Hey, Anybody intrested in a Remote Robotics Simulation Engineer Job? $180/hr - $200/hr

DM me for more # What You'll Do * Design and implement high-fidelity robot models (URDF/MJCF) with accurate kinematics, dynamics, and contact properties * Build and maintain simulation environments using MuJoCo, NVIDIA Isaac Sim, and/or Gazebo * Develop end-to-end simulation pipelines for robot training, testing, and validation * Tune physics parameters — friction, damping, inertia, actuator models — to maximize sim-to-real transfer * Integrate simulations with ROS2 for perception, planning, a

Robotics advanced Robot Programming
By: ApplicationRich5216
Source: Reddit May 19, 2026
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Open-source robot arm picking items from store shelves

A mobile retail robot using an open-source robot arm to pick items from store shelves. It’s a simple demo, but a nice example of real-world manipulation: finding the item, reaching into the shelf, gripping it, and placing it into the cart. The open-source hardware angle makes it especially interesting for robotics builders.

Robotics intermediate General Robotics
By: Pegeen-ice
Source: Reddit May 19, 2026
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Justifying bio-inspired robotics research: A taxonomy of strategies

For most of human history, we have not thought systematically about how and why we incorporate aspects of the natural world into our designs. The lack of a systematic approach has resulted in inconsistencies in motivations and methods that make it difficult to predict or evaluate the success of bio-inspired design. This mismatch between expectations and results can lead to disappointment when a reader considers a bio-inspired design to be superficial, weak, or incomplete. This is especially true in the field of Robotics, in which similarity to a biological system might be the driving motivation for construction. In an effort to assist robotics researchers justify their specific bio-inspired approach and to assist funding program managers with discerning the value of different bio-inspired approaches, here we propose a taxonomy of motivations for bio-inspired design and describe the potential significant contributions that are likely to result from different approaches.

Robotics advanced Robotics
By: Margaret J. Zhang, Justin Ting, Talia Y. Moore
Source: arXiv May 19, 2026
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DAG-Based QoS-Aware Dynamic Task Placement for Networked Multi-Stage Control Pipelines

Current Physical AI (PAI) relies heavily on closed-loop visual-servoing pipelines, whose perception and planning stages may become computationally intensive onboard due to complex models embedded on robots. In practice, offloading the perception task to on-site edges statically is inappropriate for latency-sensitive, precise industrial settings over a standardized industrial network. This emphasizes the importance of Control-Communication-Computing (3C) co-design in industrial automation: monolithic local execution saturates AI-accelerated machine and robot hardware, while static edge offloading exposes the control loop to network jitter. Existing adaptive task placement (ATP) controllers can partially address the gap by relocating a single pipeline stage on binary threshold rules, without a multi-stage model and an explicit cost on placement switching. In this Work-in-Progress (WiP) paper, we propose a directed acyclic graph (DAG) based quality-of-service (QoS)-aware dynamic task placement (DTP) framework for sensing-perception-planning-control pipelines in networked robotics. This pipeline is formalized as a DAG with task-level and node-level attributes for compute cost, communication delay, and feasible placement sets; over a small interpretable candidate set (fully local, static offload, hybrid), a window-based cost function combines tail end-to-end latency, deadline violation rate, hardware utilization, and a Hamming-distance switching penalty, and a DTP algorithm with hysteresis and a minimum dwell-time bounds placement chatter. Our WiP paper presents the theoretical framework, a structured qualitative analysis, and a two-phase simulation plus hardware-in-the-loop validation roadmap.

Robotics advanced Robotics
By: Thien Tran, Jonathan Kua, Thuong Hoang +3 more
Source: arXiv May 19, 2026
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Beyond Action Residuals: Real-World Robot Policy Steering via Bottleneck Latent Reinforcement Learning

Pretrained imitation policies have become a strong foundation for robot manipulation, but they often require online improvement to overcome execution errors, limited dataset coverage, and deployment mismatch. A central question is therefore how reinforcement learning (RL) should adapt policies after offline pretraining. Existing lightweight methods commonly apply residual corrections directly in action space, but this often leads to noisy and poorly structured exploration. In this work, we propose Z-Perturbation Reinforcement Learning (ZPRL), an approach that steers pretrained policies through a compact bottleneck latent rather than through policy weights or output actions. During offline training, we augment the policy with a plug-and-play variational information bottleneck (VIB) module to extract a task-relevant latent interface from observation embeddings. During online finetuning, the base policy is frozen and RL learns only a residual perturbation on this latent, whose decoded representation conditions the frozen action generator. We instantiate ZPRL on flow-matching policies and evaluate it on eight simulation tasks and four real-world tasks. Across diverse manipulation settings, ZPRL improves both sample efficiency and final performance over strong post-training baselines. In the real world, ZPRL improves the average success rate on four tasks by 33.7% over imitation base policies while producing smoother exploration behaviors than an action residual counterpart. These results suggest that a compact, task-aligned bottleneck latent provides an effective interface for online RL adaptation. More videos can be found at https://manutdmoon.github.io/ZPRL/.

Robotics advanced Robotics
By: Dongjie Yu, Kun Lei, Zhennan Jiang +2 more
Source: arXiv May 19, 2026
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RoHIL: Robust Human-in-the-Loop Robotic Reinforcement Learning Against Illumination Variations

Human-in-the-loop reinforcement learning systems achieve near-perfect success on the workstation where they are trained, but collapse when the same robot is moved to a workstation a few meters away due to shifts in the visual input distribution caused by new lamp positions and window light. Re-collecting demonstrations and re-running HIL on every workstation is incompatible with deployment, and naively fine-tuning on shifted-light data triggers catastrophic forgetting of the source workstation. To close this cross-domain gap, we present RoHIL, an offline fine-tuning framework that uses no extra real-robot interaction. RoHIL combines (i) a world-model-based image relighter that re-synthesises the visual stream of source-workstation trajectories under multiple virtual HDRI environments, leaving actions and rewards real; (ii) Illumination-Retention Replay (IRR), a data-level anti-forgetting mechanism that interleaves relit adaptation transitions with original-light retention transitions to preserve source-workstation Bellman coverage; and (iii) an anchored Bellman-actor regulariser that constrains representation and policy drift from the original source-workstation policy. Across four real-robot manipulation tasks under significant cross-workstation illumination variations, RoHIL substantially improves shifted-light performance where standard HIL-RL collapses, while preserving source-workstation performance, eliminating the need to re-collect data and retrain for every new workstation and environment. Project page: https://anonymous4365.github.io/RoHIL/

Robotics advanced Robotics
By: Shuoqin Zhang, Yixin Xiong, Xiru Gao +4 more
Source: arXiv May 19, 2026
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Robotics-Inspired Guardrails for Foundation Models in Socially Sensitive Domains

Foundation models are increasingly deployed in socially sensitive domains such as education, mental health, and caregiving, where failures are often cumulative and context-dependent. Existing guardrail approaches -- ranging from training-time alignment to prompting, decoding constraints, and post-hoc moderation -- primarily provide empirical risk reduction rather than enforceable behavioral guarantees, and largely treat safety as a property of individual outputs rather than interaction trajectories. We reframe guardrails as a problem of runtime behavioral control over interaction trajectories, drawing on robotics to introduce formal constructs for constraint enforcement in uncertain, closed-loop systems. We instantiate these ideas in the Grounded Observer framework and apply it across three real-world deployments: small talk, in-home autism therapy, and behavioral de-escalation in schools. Across settings, the framework enables runtime interventions that mitigate drift into undesirable interaction regimes while adapting to diverse social contexts. We discuss extensions to the framework and propose research directions toward stronger guarantees.

Robotics advanced Artificial IntelligenceRobotics
By: Rebecca Ramnauth, Drazen Brscic, Brian Scassellati
Source: arXiv May 19, 2026
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World-Ego Modeling for Long-Horizon Evolution in Hybrid Embodied Tasks

World models are widely explored in embodied intelligence, yet they typically predict distinct evolutions of the world and the ego within a single stream, where the world captures persistent instruction-agnostic scene regularities and the ego captures robot-centric instruction-conditioned dynamics. This world-ego entanglement leads to a degradation in long-horizon embodied scenarios, particularly in hybrid tasks with interleaved navigation and manipulation behaviors. In this paper, we introduce \emph{World-Ego Modeling}, a new conceptual paradigm that decomposes future evolution into world and ego components. We define the world-ego boundary from three perspectives, i.e., motion-, semantic-, and intention-based views, and analyze three disentanglement strategies with post-, pre-, and full disentanglement. Further, we instantiate this paradigm as the World-Ego Model (WEM), a unified embodied world model that couples an implicit separate world-ego planner with a cascade-parallel mixture-of-experts (CP-MoE) diffusion generator. To enable rigorous evaluation, we further construct HTEWorld, the first benchmark for long-horizon world modeling with hybrid navigation-manipulation tasks, providing 125K video clips (over 4.5M frames) with fine-grained action annotations and 300 multi-turn evaluation trajectories (over 2K instructions). Extensive experiments show that WEM achieves state-of-the-art performance on HTEWorld while remaining competitive on existing manipulation-only benchmarks.

Robotics advanced Artificial IntelligenceRobotics
By: Zuyao Lin, Jianhui Zhang, Peidong Jia +3 more
Source: arXiv May 19, 2026
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TravExplorer: Cross-Floor Embodied Exploration via Traversability-Aware 3-D Planning

Zero-shot Object Navigation (ZSON) has shown promise for open-vocabulary target search in unseen environments, yet most existing systems remain tied to planar representations and single-floor assumptions. These assumptions become inadequate in real buildings, where navigation involves floors, stairs, landings, and vertically overlapping spaces. This article presents TravExplorer, a cross-floor embodied exploration framework that couples zero-shot semantic guidance with traversability-aware 3-D planning. TravExplorer maintains a unified volumetric map that distinguishes occupied structures from robot-reachable support surfaces and extracts traversable frontiers from connected support surfaces, including floors, stairs, and landings. A FOV-aware active perception strategy further resolves incomplete observations during cross-floor traversal. To reduce semantic-reasoning latency, a lightweight guidance module aligns a probabilistic instance map from online open-vocabulary segmentation with a spatial value map from fast image-to-text matching. Based on these geometric and semantic memories, a hierarchical planner performs target-aware frontier touring over object hypotheses, traversable frontiers, and stair landmarks, and generates executable cross-floor motions through foothold-guided 3-D search and vertically constrained local trajectory optimization. Experiments over 4,195 simulated episodes on HM3D and MP3D demonstrate consistent advantages over representative ObjectNav baselines. Fifty real-world trials on a Unitree Go2 further validate open-vocabulary target search across single-floor and cross-floor indoor environments without prior maps or human intervention. The code will be released at https://github.com/wuyi2121/TravExplorer.

Robotics advanced Robotics
By: Han Zheng, Zhe Chen, Yudong Huang +4 more
Source: arXiv May 19, 2026
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CEER: Compliant End-Effector and Root Control as a Unified Interface for Hierarchical Humanoid Loco-Manipulation

Humanoid robots have achieved impressive locomotion performance, yet contact-rich and long-horizon manipulation remains a major bottleneck. Manipulation is inherently contact-rich and demands compliant whole-body control for stable interaction, while its diversity and long-horizon nature favor modular, planner-compatible interfaces over joint-space tracking. We propose CEER, a compliant end-effector-root (EE-root) control abstraction for modular humanoid loco-manipulation within a hierarchical planning framework. CEER enables compliance-aware whole-body control in an interpretable task space defined by root motion commands and end-effector pose targets, and supports plug-and-play integration with heterogeneous high-level planners. A teacher-student framework is adopted to distill a general motion-tracking controller into a low-level policy that consumes only EE-root commands. We further construct a hierarchical system that integrates heterogeneous planners and task modules through the EE-root interface, enabling diverse manipulation tasks without retraining the underlying whole-body policy. Experiments in simulation and on hardware demonstrate 3.3 cm end-effector tracking accuracy with substantially reduced jerk compared to baselines, stable contact-rich manipulation under teleoperation, and up to 70% success in simulated single-object loco-manipulation tasks within a room-scale environment. These results indicate that compliant EE-root control provides a practical abstraction for humanoid loco-manipulation, enabling modular and scalable integration of diverse skills.

Robotics advanced Robotics
By: Xinyuan Luo, Xingrui Chen, Xunjian Yin +6 more
Source: arXiv May 19, 2026
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Beyond Binary Success: A Diagnostic Meta-Evaluation Framework for Fine-Grained Manipulation

Fine-grained manipulation marks a regime where global scene context no longer suffices, and success hinges on the tight coupling of local attribute grounding, high-fidelity spatial perception, and constraint-respecting motor execution. However, current embodied AI benchmarks collapse these capacities into binary success rates, systematically inflating reported capabilities by up to 70% and masking the architectural bottlenecks that impede real-world deployment. We introduce MetaFine, a diagnostic meta-evaluation framework that disentangles manipulation competency along three axes: understanding, perception, and controlled behavior. Built on a compositional task graph, MetaFine absorbs heterogeneous external benchmarks and reconstructs them into diagnostic scenarios of varying complexity under a unified protocol. Evaluating state-of-the-art vision-language-action (VLA) models through this lens exposes severe dimension-specific failures invisible to conventional metrics. Through targeted causal intervention, we identify the visual encoder's ability to preserve local spatial structure as a key bottleneck for fine-grained precision: improving it directly unlocks previously inaccessible manipulation capabilities without modifying downstream policies. MetaFine further supports hybrid real-sim validation, using limited paired real-world rollouts to calibrate scalable simulation-based estimates for more stable physical benchmarking. By shifting evaluation from ranking to diagnosis, MetaFine turns benchmarking into an actionable compass for repairing the layered capacities underlying genuine physical dexterity. The MetaFine framework, benchmarks, and supporting resources will be publicly released at our project page: https://metafine.github.io/.

Robotics advanced Machine LearningRobotics
By: He-Yang Xu, Pengyuan Zhang, Zongyuan Ge +5 more
Source: arXiv May 19, 2026
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Minimalist Visual Inertial Odometry

Visual-Inertial Odometry(VIO), which is critical to mobile robot navigation, uses cameras with a large number of pixels. Capturing and processing camera images requires significant resources. This work presents a minimalist approach to planar odometry, demonstrating that just four visual measurements and an IMU can provide robust motion estimation for differential-drive robots. Our key insight is that four downward-facing photodiodes that sense the world through optical Gabor masks produce signals that encode speed. Based on this, we jointly optimize the mask parameters alongside a Temporal Convolutional Network (TCN) using a physically-grounded simulator. The resulting model decodes speed from just the four measurements produced by the photodiodes. Pairing these estimates with the angular speed from an IMU yields a continuous planar trajectory. We validate our approach with a prototype sensor mounted on a differential drive robot. Across diverse indoor and outdoor terrains, our system closely tracks the reference ground truth without any real-world fine-tuning. Our work shows that minimalist sensing enables efficient and accurate planar odometry.

Robotics advanced Machine LearningRobotics
By: Francesco Pasti, Jeremy Klotz, Nicola Bellotto +1 more
Source: arXiv May 19, 2026
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Towards LLM-Assisted Architecture Recovery for Real-World ROS~2 Systems: An Agent-Based Multi-Level Approach to Hierarchical Structural Architecture Reconstruction

Explicit software architecture models are essential artifacts for communicating, analyzing, and evolving complex software-intensive systems. In ROS~2-based robotic systems, however, structural (de-)composition and integration semantics are often only implicitly encoded across distributed artifacts such as source code and launch files, making recovery of hierarchical architecture particularly difficult. Existing approaches mainly focus on node-level entities and communication wiring, while providing limited support for recovering hierarchical structural (de-)composition across multiple abstraction levels. In this paper, we extend our previously proposed blueprint-guided LLM-assisted architecture recovery pipeline for ROS~2 systems through two major enhancements: (1) refined prompting to improve the consistency and controllability of architecture synthesis, and (2) a staged recovery strategy based on multi-level intermediate architectural representations that incorporate the atomic ROS node list and launch file dependencies, thereby enabling structurally constrained reconstruction across multiple abstraction levels. The approach is evaluated on a real-world automated product disassembly system based on cooperative robotic arms and heterogeneous ROS~2 artifacts. Compared to our previous work, the considered case study exhibits substantially higher integration complexity and richer functionality. The results demonstrate improved structural consistency, scalability, and robustness of architecture recovery, while also revealing remaining challenges related to dynamic integration semantics in large-scale ROS~2 systems.

Robotics advanced Artificial IntelligenceRobotics
By: Dominique Briechle, Raj Chanchad, Tobias Geger +5 more
Source: arXiv May 19, 2026
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Probing Embodied LLMs: When Higher Observation Fidelity Hurts Problem Solving

Large Language Models are increasingly proposed as cognitive components for robotic systems, yet their opaque decision processes make it difficult to explain success or failure in closed-loop embodied tasks. Following an empirical AI methodology, we study embodied LLM agents behaviorally by varying the information available to the agent and measuring the resulting changes in behavior. Using the Lockbox, a sequential mechanical puzzle with hidden interdependencies, we evaluate LLMs across RGB, RGB-D, and ground-truth symbolic observations in a physical robotic setup and use controlled simulation to probe the resulting behavior. Counterintuitively, agents perform best under raw RGB input and worst under perfect ground-truth observations. In simulation, we probe this effect by randomly flipping perceived action outcomes and find that moderate noise improves performance, peaking at a 40% flip probability with a 2.85-fold success rate increase over the noise-free baseline. Further analysis links this gain to a reduction in repetitive action loops. These findings suggest that success rates alone are insufficient for evaluating LLMs, as measured performance may reflect the interaction between perceptual errors and reasoning failures rather than robust problem solving.

Robotics advanced Artificial IntelligenceRobotics
By: Oussama Zenkri, Oliver Brock
Source: arXiv May 19, 2026
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Topology-Optimized Pneumatic Soft Actuator: Design and Experimental Validation

This paper demonstrates the computational design of soft elastomeric pneumatic actuators using nonlinear topology optimization. An existing density- and porohyperelasticity-based topology optimization framework was extended from 2D to 3D and used to generate two manufacturable actuator designs, which were then studied numerically and experimentally. For both designs, the objective was to maximize the bending response for a prescribed actuation pressure under two different allowable strain limits. A key advantage of the employed topology optimization framework is that it can consistently, during the optimization, account for the very large deformations induced upon pressurization. The two optimized 3D designs were fabricated using stereolithography and experimentally tested to validate their performance.

Robotics advanced Robotics
By: Sumit Mehta, Konstantinos Poulios
Source: arXiv May 19, 2026
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📝 blog

Geekplus Takes Fifth RBR50 Innovation Award With AI-Powered Picking Station

The warehouse robotics firm’s latest win recognises a zero-shot learning system deployed at a Schneider Electric facility in Shanghai Beijing-headquartered Geekplus (HKEX: 2590.HK) has won the 2026 RBR50 Innovation Award for its Robot Arm Picking Station, an intelligent system that automates item picking, which is widely regarded as the last major bottleneck in warehouse automation. […]

Robotics intermediate Industry NewsAutomation
By: Sam Francis
Source: Robotics and Automation News May 20, 2026
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🎥 video

Introducing the Model Guidance Dashboard in Simulink Check

Learn about the Model Guidance Dashboard in Simulink Check™ and how it is used to evaluate and improve model quality using a structured set of automated checks and recommendations. Start with discovering the purpose of the dashboard: providing a centralized view of modeling guidance and related checks that help identify structural, stylistic, and modeling guideline-related issues in Simulink® models. Rather than running individual checks in isolation, the dashboard organizes relevant analyses i

Robotics intermediate Robot Programming
By: MATLAB
Source: YouTube Robotics Channels May 20, 2026
1.3
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💻 github repo

rbot - An open-source end-to-end Autonomous Mobile Robot simulation stack for ROS 2 Jazzy and Gazebo Harmon

An open-source end-to-end Autonomous Mobile Robot simulation stack for ROS 2 Jazzy and Gazebo Harmonic. This Python project has 75 stars and 11 forks on GitHub. Topics: autonomous-navigation, autonomous-robots, autonomous-systems, mapping, robot. Homepage: https://youtu.be/B-d64c-2Mw0

Robotics intermediate ROSSLAM
By: rlxai
Source: GitHub May 9, 2026
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Will Robotics Have a ChatGPT Moment?

Over the next few decades, billions of autonomous, AI-powered robots will work alongside people in factories, perform tedious tasks in warehouses, care for the elderly, assist in unsafe disaster areas, deliver packages and food to our doorsteps, and eventually, help out in our homes. Some will look like us, and many won’t. What is certain is that regardless of form factor, robots will all rely heavily on AI in order to deliver real-world value.In 2025, total investments in robotics companies ...

Robotics intermediate Robotics ResearchIndustry News
By: Hans Peter Brondmo
Source: IEEE Spectrum Robotics May 20, 2026
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5 lessons for robotics entrepreneurs from Aescape’s business model pivot

Aescape has pivoted from RaaS to 'platform-powered robots,' restructuring to turn its robotic massage tables into a scalable 'execution machine.' The post 5 lessons for robotics entrepreneurs from Aescape’s business model pivot appeared first on The Robot Report.

Robotics intermediate Industry NewsMarket Analysis
By: Mike Oitzman
Source: The Robot Report May 15, 2026
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Cirtronics to discuss manufacturing robotics at scale at the Robotics Summit

Cirtronics is exhibiting and presenting an expert panel at the Robotics Summit & Expo on how to successfully commercialize robotics. The post Cirtronics to discuss manufacturing robotics at scale at the Robotics Summit appeared first on The Robot Report.

Robotics intermediate Industry NewsMarket Analysis
By: Brianna Wessling
Source: The Robot Report May 15, 2026
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North American robot orders remain flat at the start of 2026

A3 said its Q1 data reinforces a broader industry trend: robotics adoption is expanding well beyond traditional automotive manufacturing. The post North American robot orders remain flat at the start of 2026 appeared first on The Robot Report.

Robotics intermediate Industry NewsMarket Analysis
By: The Robot Report Staff
Source: The Robot Report May 15, 2026
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How agentic AI can enable general-purpose robotic navigation

Learn how agentic AI enables robotic navigation by combining perception, SLAM, reasoning, and planning in dynamic environments. The post How agentic AI can enable general-purpose robotic navigation appeared first on The Robot Report.

Robotics intermediate Industry NewsMarket Analysis
By: Pratik Shinde
Source: The Robot Report May 16, 2026
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Why the world’s most successful robotics founders are systems thinkers

Join Ajay Agarwal as he reveals how systems thinking in robotics unlocks massive productivity and multibillion-dollar markets. The post Why the world’s most successful robotics founders are systems thinkers appeared first on The Robot Report.

Robotics intermediate Industry NewsMarket Analysis
By: Mike Oitzman
Source: The Robot Report May 16, 2026
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Fraunhofer IPA offers new test benchmark for humanoids

To be used in industrial applications, humanoid robots must meet application-relevant criteria. Fraunhofer IPA has developed a benchmark for third-party analysis. The post Fraunhofer IPA offers new test benchmark for humanoids appeared first on The Robot Report.

Robotics intermediate Industry NewsMarket Analysis
By: Dr. Werner Kraus, Simon Schmidt, and Dr. Theo Jacobs, Fraunhofer IPA
Source: The Robot Report May 17, 2026
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FANUC strengthens robot integration with NVIDIA Isaac Sim

FANUC is integrating its robots and teach pendant with NVIDIA simulation and AI technology for smoother, smarter action. The post FANUC strengthens robot integration with NVIDIA Isaac Sim appeared first on The Robot Report.

Robotics intermediate Industry NewsMarket Analysis
By: The Robot Report Staff
Source: The Robot Report May 18, 2026
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Locus Array expands picking ability with Nexera Robotics’ NeuraGrasp

Nexera Robotics' adaptive NeuraGrasp will enable the Locus Array mobile manipulator to pick many more e-commerce SKUs. The post Locus Array expands picking ability with Nexera Robotics’ NeuraGrasp appeared first on The Robot Report.

Robotics intermediate Industry NewsMarket Analysis
By: Eugene Demaitre
Source: The Robot Report May 19, 2026
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Learn about the latest in logistics automation at the Robotics Summit

The 2026 Robotics Summit & Expo, which takes place May 27 and 28, will feature an entire session track focused on robots in logistics. The post Learn about the latest in logistics automation at the Robotics Summit appeared first on The Robot Report.

Robotics intermediate Industry NewsMarket Analysis
By: Brianna Wessling
Source: The Robot Report May 19, 2026
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Opentrons launches Compliance Ready Software for Flex lab robots

Opentrons says that 10,000 laboratory robots deployed worldwide can comply with strict FDA regulations thanks to its new software. The post Opentrons launches Compliance Ready Software for Flex lab robots appeared first on The Robot Report.

Robotics intermediate Industry NewsMarket Analysis
By: The Robot Report Staff
Source: The Robot Report May 19, 2026
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Regal Rexnord to bring its motion control portfolio to the Robotics Summit

At the event, Regal Rexnord will demonstrate how its unified motion ecosystem supports modern robotic system design. The post Regal Rexnord to bring its motion control portfolio to the Robotics Summit appeared first on The Robot Report.

Robotics intermediate Industry NewsMarket Analysis
By: The Robot Report Staff
Source: The Robot Report May 19, 2026
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CVPR 2026 to showcase next generation of embodied AI, robotics, and autonomous systems

This year’s CVPR – Conference on Computer Vision and Pattern Recognition – will bring together more than 100 technology companies, AI developers, robotics firms, and autonomous systems specialists in Denver next month as the event highlights the latest advances in embodied AI, machine vision, robotics, and autonomous technologies. Co-sponsored by IEEE Computer Society and the […]

Robotics intermediate Industry NewsAutomation
By: David Edwards
Source: Robotics and Automation News May 19, 2026
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OLO Robotics completes commercial launch with three international manufacturing and distribution partnerships

OLO Robotics has completed its commercial launch, announcing new manufacturing and distribution partnerships with Deep Robotics, inMotion Robotic, and Fiction Lab as robot companies move to make their hardware accessible to mainstream software teams, not only specialist roboticists. By integrating OLO’s ROS2-native platform with quadrupeds and mobile robots, these partners are positioning robots as part […]

Robotics intermediate Industry NewsAutomation
By: David Edwards
Source: Robotics and Automation News May 20, 2026
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Boston Dynamics trains Atlas humanoid robot to pick up and place washing machine

Boston Dynamics has released new behind-the-scenes footage showing its latest electric humanoid robot, Atlas, performing heavy lifting and manipulation tasks that the company says are designed to prepare the system for real industrial work. (See video below.) The demonstration centers on Atlas lifting and carrying a mini-fridge weighing roughly 50 pounds, although Boston Dynamics says […]

Robotics intermediate Industry NewsAutomation
By: David Edwards
Source: Robotics and Automation News May 20, 2026
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Robotics Café brings together autonomous robot practitioners

The recently launched Robotics Café is a weekly online seminar series to bring together researchers, students and industry practitioners working in the field of autonomous robotics. One of the key aims of the initiative is to provide a dedicated platform for students to present and disseminate their work, enabling broader visibility and impact across academia […]

Robotics intermediate Robotics ResearchIndustry News
By: Lucy Smith
Source: Robohub May 20, 2026
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