Robotics

BioflexBot robot hand aims to replicate key human hand motions

Humanoid robot developers are trying to replicate the human hand. Source: Adobe Stock Human hands are dexterous, versatile machines. Roboticists have tried to copy the biological structures of the hand for humanoid grippers. This, however, typically results in complex and difficult-to-control structures. Researchers proposed an alternative end-effector solution in a new study published by Wiley in Advanced Science. They created the BioflexBot, a novel robot that mimics and even exceeds core moti

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Researchers have developed BioflexBot, a robot hand that replicates key human hand motions using a simple design based on a coiled spring, constraining shell, and pneumatic system rather than mimicking the complex anatomy of an actual human hand. Instead of trying to copy the human form, the design focuses on capturing fundamental functions like pinching, rotating, hooking, and grasping with just two pneumatic inputs. The BioflexBot exceeds human hand performance in certain capabilities, including extending and contracting 3.5 times more than a human hand and grasping objects up to almost 13 times bigger than similar systems. Researchers demonstrated potential applications including inspecting aeroengine blades, completing daily tasks integrated with a humanoid robot, and conducting chemistry experiments, suggesting the simple design can deliver high dexterity at extremely low costs.

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Google DeepMind releases new advanced robotics AI model and next-generation gene prediction model

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Gemini Robotics 2.5 accelerates real-world general dexterity

Our most capable vision-language-action model enhances robot dexterity, reliability, and safety

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Can AI agents program industrial robots without training data? Berkeley says yes

The new RoboSimStudio is an open-source project that enables agentic AI coding of robotic simulation environments. | Credit: UC Berkeley UC Berkeley and NVIDIA have released open-source tools for a new approach: enabling teams of AI coding agents to write, simulate, and refine readable robot programs. Since the early days of industrial robots, programming, tuning, and troubleshooting robotic workcells has taken weeks to months. New research coming out of UC Berkeley has demonstrated that new age

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Boston Dynamics adds wheels to Atlas humanoid to test whole-body control

Bridging the divide between deterministic physics-based control and policy-driven adaptation, Boston Dynamics has deployed a hybrid locomotion architecture on a wheeled variant of its Atlas humanoid. The “experiment” combined classical model-based motor control with whole-body reinforcement learning to maintain dynamic stability across unstructured, real-world terrain. In the video, Andy Wu, controls engineer for Atlas at Boston Dynamics, highlights some recent experiments that he

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Practice Makes Precise: At CoRL, NVIDIA and Partners Reveal How Robots Rehearse Delicate Work in Simulation

Simulation, which has long been the proving ground for robots learning to walk, roll and hop, is now helping researchers teach robots tasks that require a finer touch: aligning gears, connecting cables and assembling small components.  The shift from locomotion to manipulation is taking center stage at this week’s Conference on Robot Learning (CoRL) in Austin, Texas, alongside the rise of agentic robotics — a new paradigm that integrates frontier agents to create interpretable code for physical

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Memories.ai builds standardized egocentric video pipeline

Memories.ai models label every clip: Memories-H0 for hand pose, EgoAction for action segments. | Credit: Memories.ai Memories.ai is building a data pipeline for robotics companies that combines standardized video collection with AI-powered annotation to turn everyday egocentric footage into training data for robots. The San Francisco-based company is developing software and AI to help robots understand the physical world. It builds video-understanding models and data infrastructure, and it colle